References

Lecture 01.1 (Historical Body Models)

1. Johansson, G. (1973). Visual perception of biological motion and a model for its analysis. Perception & Psychophysics, 14(2), 201–211.

2. Marr, D., & Nishihara, H. (1978). Representation and recognition of the spatial organization of three-dimensional shapes. Proc. R. Soc. Lond. B, 200(1140), 269–294.

3. Nevatia, R., & Binford, T. (1977). Description and recognition of curved objects. Artificial Intelligence, 8, 77–98.

4. O’Rourke, J., & Badler, N. (1980). Model-based image analysis of human motion using constraint propagation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2(6), 522–532.

5. Hogg, D. (1983). Model-based vision: A program to see a walking person. Image and Vision Computing, 1(1), 5–20.

6. Metaxas, D., & Terzopoulos, D. (1993). Shape and nonrigid motion estimation through physics-based synthesis. IEEE Trans. on Pattern Analysis and Machine Intelligence, 15(6), 580–591.

7. Gavrila, D., & Davis, L. (1996). 3-D model-based tracking of humans in action: A multi-view approach. Proc. CVPR, 73–80.

8. Bregler, C., & Malik, J. (1998). Tracking people with twists and exponential maps. Proc. CVPR, 8–15.

9. Blanz, V., & Vetter, T. (1999). A morphable model for the synthesis of 3D faces. Proc. SIGGRAPH ‘99, 187–194.

10. CAESAR Project Report (1999). 3D body scans of ~4,000 individuals.

11. Allen, B., Curless, B., & Popović, Z. (2003). The space of human body shapes: Reconstruction and parameterization from range scans. ACM SIGGRAPH, 587–594.

12. Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005). SCAPE: Shape Completion and Animation of People. ACM SIGGRAPH, 408–416.

13. Hasler, N., Stoll, C., Sunkel, M., Rosenhahn, B., & Seidel, H.-P. (2009). A statistical model of human pose and body shape. Eurographics 2009.

14. Sigal, L., Balan, A., & Black, M. (2010). Humaneva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. IJCV, 87(1), 4–27.

15. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M. J. (2015). SMPL: A Skinned Multi-Person Linear Model. ACM Transactions on Graphics, 34(6), 248:1–16.

16. Pons-Moll, G., Pujades, S., Hu, S., & Black, M. J. (2015). Dyna: A model of dynamic human shape in motion. ACM Transactions on Graphics, 34(4), 120:1–14.

17. Bogo, F., et al. (2016). Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image. ECCV 2016.

18. Kanazawa, A., Black, M. J., Jacobs, D., & Malik, J. (2018). End-to-end recovery of human shape and pose. CVPR 2018.

19. Kato, H., Ushiku, Y., & Harada, T. (2018). Neural 3D Mesh Renderer. CVPR 2018.

20. Saito, S., Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., & Li, H. (2019). PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization. ICCV 2019.

21. Shysheya, A., Zakharov, E., et al. (2019). Textured Neural Avatars. CVPR 2019.

22. Deng, B., et al. (2020). NASA: Neural Articulated Shape Approximation. ECCV 2020.

23. Recent Works: Various papers (2020–2022) on neural implicit representations, NeRF-based human modeling, and neural avatars.

Lecture 01.2 (Introduction to Human Models)

1. Allen, B., Curless, B., & Popović, Z. (2003). The space of human body shapes: Reconstruction and parameterization from range scans. ACM SIGGRAPH, 587–594.

2. Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005). SCAPE: Shape Completion and Animation of People. ACM SIGGRAPH, 408–416.

3. Hirshberg, D., Loper, M., Rachlin, E., & Black, M. (2012). Coregistration: Simultaneous alignment and modeling of articulated 3D shape. ECCV, 242–255.

4. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M. J. (2015). SMPL: A Skinned Multi-Person Linear Model. ACM Transactions on Graphics, 34(6), Article 248.

5. Pons-Moll, G., Pujades, S., Hu, S., & Black, M. J. (2017). ClothCap: Seamless 4D clothing capture and retargeting. ACM Transactions on Graphics, 36(4), Article 73.

6. Pons-Moll, G., Taylor, J., & Romero, J. (2015). Dyna: A Model of Dynamic Human Shape in Motion. ACM Transactions on Graphics, 34(4), 120:1–14.

7. Allen, B., Curless, B., Popović, Z., & Hertzmann, A. (2006). Learning a correlated model of identity and pose-dependent body shape variation for real-time synthesis. Proc. SCA, 147–156.

8. Chen, Y., Liu, Z., & Zhang, Z. (2013). Tensor-based human body modeling. CVPR, 105–112.

9. Hasler, N., Stoll, C., Sunkel, M., Rosenhahn, B., & Seidel, H.-P. (2009). A statistical model of human pose and body shape. Eurographics.

10. Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., & Black, M. (2016). Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image. ECCV, 561–578.

11. Kanazawa, A., Black, M.J., Jacobs, D., & Malik, J. (2018). End-to-End Recovery of Human Shape and Pose. CVPR, 7122–7131.

12. Deng, B., Liu, L., Dong, Y., Chang, M., & Cai, J. (2020). NASA: Neural Articulated Shape Approximation. ECCV 2020.

13. Hanavan, E.P. (1964). A Mathematical Model of the Human Body. Technical Report, Air Force Aerospace Medical Research Lab.

14. Kuipers, J.B. (2002). Quaternions and Rotation Sequences: A Primer with Applications to Orbits, Aerospace and Virtual Reality. Princeton University Press.

15. Park, S.I., & Hodgins, J.K. (2008). Data-driven modeling of skin and muscle deformation. ACM Transactions on Graphics, 27(3), Article 96.

Lecture 01.3 (Introduction to Human Models Continued)

1. Weber Brothers (1836). Early gait analysis (historical references).

2. Baraff, D. & Witkin, A. (1998). Large steps in cloth simulation. SIGGRAPH.

3. Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., & Black, M. (2016). Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image. ECCV.

4. Cao, Z., Simon, T., Wei, S. E., & Sheikh, Y. (2017). Realtime multi-person 2D pose estimation using part affinity fields. CVPR.

5. Pons-Moll, G., Pujades, S., Hu, S., & Black, M. J. (2017). ClothCap: Seamless 4D clothing capture and retargeting. ACM TOG (SIGGRAPH).

6. Park, J. J., Florence, P., Straub, J., Newcombe, R., & Lovegrove, S. (2019). DeepSDF: Learning continuous signed distance functions for shape representation. CVPR.

7. Delp, S. L., et al. OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement.

8. Deng, B., et al. (2020). NASA: Neural Articulated Shape Approximation. ECCV.

9. Güler, R. A., Neverova, N., & Kokkinos, I. (2018). DensePose: Dense human pose estimation in the wild. CVPR.

10. Hassan, M., et al. (2019). Resolving 3D Human Pose Ambiguities with 3D Scene Constraints. 3DV.

11. Hodgins, J., Wooten, W., Brogan, D., & O’Brien, J. (1995). Animating human athletics. SIGGRAPH.

12. Kato, H., Ushiku, Y., & Harada, T. (2018). Neural 3D mesh renderer. CVPR.

13. Kanazawa, A., Black, M. J., Jacobs, D. W., & Malik, J. (2018). End-to-end recovery of human shape and pose. CVPR.

14. Kocabas, M., et al. (2020). VIBE: Video inference for human body pose and shape estimation. CVPR.

15. Marey, E.-J., Muybridge, E. (1880s). Chronophotography and motion studies.

16. Mordatch, I., et al. (2012). Discovery of complex behaviors through contact-invariant optimization. SIGGRAPH.

17. Peng, X. B. & van de Panne, M. (2018). DeepMimic: Example-guided deep reinforcement learning of physics-based character skills. SIGGRAPH.

18. Pons-Moll, G. et al. (2015). Dyna: A model of dynamic human shape in motion. ACM TOG (SIGGRAPH).

19. Anguelov, D., et al. (2005). SCAPE: Shape completion and animation of people. SIGGRAPH.

20. Shoemake, K. (1985). Animating rotation with quaternion curves. SIGGRAPH.

21. SMPL references: Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M. J. (2015). SMPL: A Skinned Multi-Person Linear Model. ACM TOG (SIGGRAPH Asia).

22. TailorNet references: Patel, M., et al. (2020). TailorNet: Predicting clothing in 3D as a function of human pose, shape and garment style. CVPR.

23. VIBE references: Kocabas, M. (2020). VIBE: Video Inference for Human Body Pose and Shape Estimation. CVPR.

24. Wang, N., et al. (2021). Various references on neural implicit representations for clothing.

25. Xie, F., et al. (2021). Physics-based motion correction. (arXiv / conference).

Lecture 02.1 (Image Formation)

1. Hartley, R., & Zisserman, A. (2004). Multiple View Geometry in Computer Vision, 2nd ed. Cambridge University Press.

2. Zhang, Z. (2000). A Flexible New Technique for Camera Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330-1334.

3. OpenCV Documentation — Lens Distortion and Calibration.

4. Levoy, M. (Stanford). Digital Photography course notes/lectures.

5. Collins, R. Camera Models in Computer Vision, lecture/course slides.

6. Alhazen (Ibn al-Haytham) (~1021). Book of Optics. English translation and commentary by A.I. Sabra, 1989.

7. Kemp, M. (1990). The Science of Art: Optical Themes in Western Art from Brunelleschi to Seurat. Yale University Press.

8. Niépce, J. N. (1826). Heliography, Earliest surviving photograph: View from the Window at Le Gras.

9. Adelson, E. H., & Bergen, J. R. (1991). The Plenoptic Function and the Elements of Early Vision. Computational Models of Visual Processing, MIT Press.

10. Ng, R., et al. (2005). Light Field Photography with a Hand-Held Plenoptic Camera. Computer Science Technical Report, Stanford.

11. Duarte, M. F., et al. (2008). Single-Pixel Imaging via Compressive Sampling. IEEE Signal Processing Magazine, 25(2), 83–91.

12. Velten, A., et al. (2012). Recovering Three-Dimensional Shape around a Corner using Ultrafast Time-of-Flight Imaging. Nature Communications, 3:745.

13. Herman, G. H. (1980). Image Reconstruction from Projections. Academic Press.

14. Kajiya, J. T. (1986). The Rendering Equation. Proc. SIGGRAPH.

15. Forsyth, D. A., & Ponce, J. (2012). Computer Vision: A Modern Approach, 2nd Edition. Prentice Hall.

16. Szeliski, R. (2010). Computer Vision: Algorithms and Applications. Springer.

17. Trucco, E., & Verri, A. (1998). Introductory Techniques for 3-D Computer Vision. Prentice Hall.

18. Raskar, R., & Tumblin, J. (2009). Computational Photography: Mastering New Techniques for Lenses, Lighting, and Sensors. A K Peters.

19. Levoy, M., & Hanrahan, P. (1996). Light Field Rendering. Proc. SIGGRAPH.

20. Ihrke, I., Kutulakos, K., Lensch, H., Magnor, M., & Heidrich, W. (2010). Transparent and Specular Object Reconstruction. Computer Graphics Forum, 29(8), 2400-2426.

Lecture 02.2 (Rotations & Kinematic Chains)

Rotations & so(3)

1. Kuipers, J.B. (2002). Quaternions and Rotation Sequences. Princeton University Press.

2. Craig, J.J. (2005). Introduction to Robotics: Mechanics and Control. Pearson.

3. Shoemake, K. (1985). Animating Rotation with Quaternion Curves. SIGGRAPH.

4. NASA Technical Notes (1968). On gimbal lock (Apollo Missions).

5. Rodrigues’ Rotation Formula, Exponential Map for so(3) (n.d.). https://en.wikipedia.org/wiki/Rodrigues%27_rotation_formula

Kinematic Chains

1. Bregler, C. (1998). Articulated Body Tracking. ICCV.

2. Modern Robotics website (n.d.). http://modernrobotics.northwestern.edu

3. Siciliano, B., & Khatib, O. (eds) (2016). Handbook of Robotics. Springer.

Axis-Angle & Non-Unit Axis

1. Hartley, R., & Zisserman, A. (2004). Multiple View Geometry, 2nd ed. Cambridge University Press.

2. Grassia, F.S. (1998). Practical parameterization of rotations using the exponential map. JGT.

Lecture 03.1 (Surface Representations)

1. Angel, E. (2008). Interactive Computer Graphics. Addison-Wesley.

2. Botsch, M., et al. (2010). Polygon Mesh Processing. A K Peters.

3. Curless, B., & Levoy, M. (1996). A Volumetric Method for Building Complex Models from Range Images. SIGGRAPH.

4. do Carmo, M. (1976). Differential Geometry of Curves and Surfaces. Prentice-Hall.

5. Rusinkiewicz, S., & Levoy, M. (2001). Efficient Variants of the ICP Algorithm. 3DIM.

6. Piegl, L., & Tiller, W. (1997). The NURBS Book. Springer.

7. Park, J., et al. (2019). DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. CVPR.

8. Mildenhall, B., et al. (2020). NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. ECCV.

9. Osher, S., & Fedkiw, R. (2003). Level Set Methods and Dynamic Implicit Surfaces. Springer.

10. Kobbelt, L., & Botsch, M. (2004). A Survey of Point-Based Techniques in Computer Graphics. Computers & Graphics.

11. Crane, K., de Goes, F., Desbrun, M., & Schröder, P. (2013). Digital Geometry Processing with Discrete Exterior Calculus. ACM SIGGRAPH Courses.

12. Barr, A. (1981). Superquadrics and Angle-Preserving Transformations. IEEE Computer Graphics and Applications.

Lecture 03.2 (Procrustes Alignment)

1. Dryden, I.L., & Mardia, K.V. (2016). Statistical Shape Analysis. Wiley.

2. Cootes, T.F. (1992). Active Shape Models. ECCV.

3. Gower, J.C. (1975). Generalized Procrustes Analysis. Psychometrika.

4. Loper, M., et al. (2015). SMPL: A Skinned Multi-Person Linear Model. ACM TOG (SIGGRAPH Asia).

5. Hartley, R., & Zisserman, A. (2004). Multiple View Geometry in Computer Vision. Cambridge University Press.

Lecture 04.1 (Iterative Closest Points)

1. Besl, P.J., & McKay, N.D. (1992). A Method for Registration of 3-D Shapes. IEEE TPAMI.

2. Chen, Y., & Medioni, G. (1992). Object Modeling by Registration of Multiple Range Images. SIGGRAPH.

3. Arun, K.S., Huang, T.S., & Blostein, S.D. (1987). Least-Squares Fitting of Two 3-D Point Sets. IEEE TPAMI.

4. Horn, B.K.P. (1987). Closed-Form Solution of Absolute Orientation Using Unit Quaternions. JOSA.

5. Rusinkiewicz, S., & Levoy, M. (2001). Efficient Variants of the ICP Algorithm. 3DIM.

6. Segal, A., Haehnel, D., & Thrun, S. (2009). Generalized-ICP. Robotics: Science and Systems.

7. Amberg, B., Romdhani, S., & Vetter, T. (2007). Optimal Step Nonrigid ICP Algorithm. CVPR.

8. Myronenko, A., & Song, X. (2010). Point Set Registration: Coherent Point Drift. IEEE TPAMI.

9. Open3D Documentation (n.d.). http://www.open3d.org.

10. SciPy Spatial Module Documentation (n.d.). https://docs.scipy.org/doc/scipy/reference/spatial.html.

11. Newcombe, R.A., et al. (2011). KinectFusion: Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera. UIST.

12. Chetverikov, D., et al. (2002). The Trimmed Iterative Closest Point Algorithm. ICPR.

13. Granger, S., & Pennec, X. (2002). Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration. ECCV.

14. Rangarajan, A., et al. (1997). Softassign Procrustes Matching Algorithm. IPMI.

Lecture 04.2 (Body Models)

1. Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005). SCAPE: Shape Completion and Animation of People. ACM SIGGRAPH.

2. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M. J. (2015). SMPL: A Skinned Multi-Person Linear Model. ACM Transactions on Graphics.

3. Hartley, R., & Zisserman, A. (2004). Multiple View Geometry in Computer Vision. 2nd Edition, Cambridge University Press.

4. Besl, P.J., & McKay, N.D. (1992). A Method for Registration of 3-D Shapes. IEEE TPAMI.

5. Chen, Y., & Medioni, G. (1992). Object Modeling by Registration of Multiple Range Images. SIGGRAPH.

6. Arun, K.S., Huang, T.S., & Blostein, S.D. (1987). Least-Squares Fitting of Two 3-D Point Sets. IEEE TPAMI.

7. Kuipers, J.B. (1999). Quaternions and Rotation Sequences. Princeton University Press.

8. Spong, M.W., Hutchinson, S., & Vidyasagar, M. (2006). Robot Modeling and Control. Wiley.

9. Myronenko, A., & Song, X. (2009). Point Set Registration: Coherent Point Drift. NIPS.

10. Pons-Moll, G., et al. (2023). Training a Body Model and Fitting SMPL to Scans. Virtual Humans (Lecture 5.1).

11. Kanazawa, A., Black, M.J., Jacobs, D., & Malik, J. (2018). End-to-End Recovery of Human Shape and Pose. CVPR.

12. Szeliski, R. (2010). Computer Vision: Algorithms and Applications. Springer.

Lecture 05.1 (Body Model Training)

1. Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005). SCAPE: Shape Completion and Animation of People. ACM Transactions on Graphics, 24(3), 408-416.

2. Hirshberg, D.A., Loper, M., Rachlin, E., & Black, M.J. (2012). Coregistration: Simultaneous Alignment and Modeling of Articulated 3D Shape. European Conference on Computer Vision (ECCV), 242-255.

3. Besl, P.J., & McKay, N.D. (1992). A Method for Registration of 3-D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 239-256.

4. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M.J. (2015). SMPL: A Skinned Multi-Person Linear Model. ACM Transactions on Graphics, 34(6), 248:1-248:16.

5. Hirshberg, D.A., Loper, M., Rachlin, E., & Black, M.J. (2012). Coregistration: Simultaneous Alignment and Modeling of Articulated 3D Shape. European Conference on Computer Vision (ECCV), 242-255.

6. Geman, S., & McClure, D.E. (1987). Statistical Methods for Tomographic Image Reconstruction. Bulletin of the International Statistical Institute, 52(4), 5-21.

7. Allen, B., Curless, B., & Popović, Z. (2003). The Space of Human Body Shapes: Reconstruction and Parameterization from Range Scans. ACM Transactions on Graphics, 22(3), 587-594.

8. Sorkine, O., & Alexa, M. (2007). As-Rigid-As-Possible Surface Modeling. Symposium on Geometry Processing, 109-116.

9. Amberg, B., Romdhani, S., & Vetter, T. (2007). Optimal Step Nonrigid ICP Algorithms for Surface Registration. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1-8.

10. Myronenko, A., & Song, X. (2010). Point Set Registration: Coherent Point Drift. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(12), 2262-2275.

11. Pons-Moll, G., et al. (2015). Dyna: A Model of Dynamic Human Shape in Motion. ACM Transactions on Graphics, 34(4), 120:1-120:14.

12. Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., & Black, M.J. (2016). Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image. European Conference on Computer Vision (ECCV), 561-578.

13. Feng, A., Casas, D., & Shapiro, A. (2015). Avatar Reshaping and Automatic Rigging Using a Deformable Model. Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games, 57-64.

14. Joo, H., Simon, T., & Sheikh, Y. (2018). Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 8320-8329.

Lecture 05.2 (3D Registration)

Rigid Registration and ICP

1. Horn, B.K.P. (1987). Closed-Form Solution of Absolute Orientation Using Unit Quaternions. Journal of the Optical Society of America A, 4(4), 629-642.

2. Arun, K.S., Huang, T.S., & Blostein, S.D. (1987). Least-Squares Fitting of Two 3-D Point Sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(5), 698-700.

3. Umeyama, S. (1991). Least-Squares Estimation of Transformation Parameters Between Two Point Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(4), 376-380.

4. Besl, P.J., & McKay, N.D. (1992). A Method for Registration of 3-D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 239-256.

5. Chen, Y., & Medioni, G. (1991). Object Modeling by Registration of Multiple Range Images. Proceedings of IEEE International Conference on Robotics and Automation, 2724-2729.

6. Zhang, Z. (1994). Iterative Point Matching for Registration of Free-Form Curves and Surfaces. International Journal of Computer Vision, 13(2), 119-152.

7. Rusinkiewicz, S., & Levoy, M. (2001). Efficient Variants of the ICP Algorithm. Proceedings of the Third International Conference on 3D Digital Imaging and Modeling, 145-152.

8. Yang, J., Li, H., Campbell, D., & Jia, Y. (2016). Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), 2241-2254.

Non-Rigid Registration

9. Gold, S., Rangarajan, A., Lu, C.P., Pappu, S., & Mjolsness, E. (1998). New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence. Pattern Recognition, 31(8), 1019-1031.

10. Chui, H., & Rangarajan, A. (2003). A New Point Matching Algorithm for Non-Rigid Registration. Computer Vision and Image Understanding, 89(2-3), 114-141.

11. Myronenko, A., & Song, X. (2010). Point Set Registration: Coherent Point Drift. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(12), 2262-2275.

12. Amberg, B., Romdhani, S., & Vetter, T. (2007). Optimal Step Nonrigid ICP Algorithms for Surface Registration. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1-8.

13. Sumner, R.W., Schmid, J., & Pauly, M. (2007). Embedded Deformation for Shape Manipulation. ACM Transactions on Graphics (SIGGRAPH), 26(3), Article 80.

14. Li, H., Adams, B., Guibas, L.J., & Pauly, M. (2009). Robust Single-View Geometry and Motion Reconstruction. ACM Transactions on Graphics (SIGGRAPH Asia), 28(5), Article 175.

15. Feldmar, J., & Ayache, N. (1996). Rigid, Affine and Locally Affine Registration of Free-Form Surfaces. International Journal of Computer Vision, 18(2), 99-119.

16. Bogo, F., Romero, J., Loper, M., & Black, M.J. (2014). FAUST: Dataset and Evaluation for 3D Mesh Registration. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3794-3801.

Parametric Models and SMPL

17. Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005). SCAPE: Shape Completion and Animation of People. ACM Transactions on Graphics (SIGGRAPH), 24(3), 408-416.

18. Allen, B., Curless, B., & Popović, Z. (2003). The Space of Human Body Shapes: Reconstruction and Parameterization from Range Scans. ACM Transactions on Graphics (SIGGRAPH), 22(3), 587-594.

19. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M.J. (2015). SMPL: A Skinned Multi-Person Linear Model. ACM Transactions on Graphics (SIGGRAPH Asia), 34(6), Article 248.

20. Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., & Black, M.J. (2016). Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image. Proceedings of European Conference on Computer Vision (ECCV), 561-578.

21. Hirshberg, D.A., Loper, M., Rachlin, E., & Black, M.J. (2012). Coregistration: Simultaneous Alignment and Modeling of Articulated 3D Shape. Proceedings of European Conference on Computer Vision (ECCV), 242-255.

22. Hasler, N., Stoll, C., Sunkel, M., Rosenhahn, B., & Seidel, H.P. (2009). A Statistical Model of Human Pose and Body Shape. Computer Graphics Forum (Eurographics), 28(2), 337-346.

Clothing and SMPL+D

23. Alldieck, T., Magnor, M., Xu, W., Theobalt, C., & Pons-Moll, G. (2019). Learning to Reconstruct People in Clothing from a Single RGB Camera. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1175-1186.

24. Ma, Q., Yang, J., Ranjan, A., Pujades, S., Pons-Moll, G., Tang, S., & Black, M.J. (2020). Learning to Dress 3D People in Generative Clothing. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6469-6478.

25. Pons-Moll, G., Pujades, S., Hu, S., & Black, M.J. (2017). ClothCap: Seamless 4D Clothing Capture and Retargeting. ACM Transactions on Graphics (SIGGRAPH), 36(4), Article 73.

26. Yang, J., Franco, J.S., Hétroy-Wheeler, F., & Wuhrer, S. (2018). Analyzing Clothing Layer Deformation Statistics of 3D Human Motions. Proceedings of European Conference on Computer Vision (ECCV), 237-253.

Modern Learning-Based Methods

27. Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., & Aubry, M. (2018). 3D-CODED: 3D Correspondences by Deep Deformation. Proceedings of European Conference on Computer Vision (ECCV), 230-246.

28. Bhatnagar, B.L., Tiwari, G., Theobalt, C., & Pons-Moll, G. (2020). IPNet: Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction. Proceedings of European Conference on Computer Vision (ECCV), 311-329.

29. Bhatnagar, B.L., Xie, X., Petrov, I., Sminchisescu, C., Theobalt, C., & Pons-Moll, G. (2020). LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration. Advances in Neural Information Processing Systems (NeurIPS), 33.

30. Chen, X., Zheng, Y., Black, M.J., Hilliges, O., & Geiger, A. (2021). SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes. Proceedings of International Conference on Computer Vision (ICCV), 11594-11604.

31. Deng, B., Lewis, J.P., Jeruzalski, T., Pons-Moll, G., Hinton, G., Norouzi, M., & Tagliasacchi, A. (2020). NASA Neural Articulated Shape Approximation. Proceedings of European Conference on Computer Vision (ECCV), 612-628.

32. Corona, E., Pumarola, A., Alenyà, G., Pons-Moll, G., & Moreno-Noguer, F. (2022). Learned Vertex Descent: A New Direction for 3D Human Model Fitting. Proceedings of European Conference on Computer Vision (ECCV), 716-734.

33. Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., & Xiang, Y. (2020). Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images. Proceedings of European Conference on Computer Vision (ECCV), 55-71.

34. Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. Proceedings of European Conference on Computer Vision (ECCV), 405-421.

35. Saito, S., Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., & Li, H. (2019). PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization. Proceedings of International Conference on Computer Vision (ICCV), 2304-2314.

Datasets

36. Bogo, F., Romero, J., Loper, M., & Black, M.J. (2014). FAUST: Dataset and Evaluation for 3D Mesh Registration. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3794-3801.

37. Ma, Q., Yang, J., Ranjan, A., Pujades, S., Pons-Moll, G., Tang, S., & Black, M.J. (2020). Learning to Dress 3D People in Generative Clothing. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6469-6478.

38. Turk, G., & Levoy, M. (1994). The Stanford 3D Scanning Repository. Stanford University Computer Graphics Laboratory.

39. Zhou, Q.Y., Park, J., & Koltun, V. (2016). Fast Global Registration. Proceedings of European Conference on Computer Vision (ECCV), 766-782.

40. Newcombe, R.A., Fox, D., & Seitz, S.M. (2015). DynamicFusion: Reconstruction and Tracking of Non-rigid Scenes in Real-Time. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 343-352.

Lecture 06.1 (Fitting SMPL to Images)

1. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M. J. (2015). SMPL: A Skinned Multi-Person Linear Model. ACM Transactions on Graphics, 34(6), 248:1–16.

2. Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., & Black, M. (2016). Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image. ECCV 2016.

3. Hartley, R., & Zisserman, A. (2004). Multiple View Geometry in Computer Vision, 2nd ed. Cambridge University Press.

4. Pavlakos, G., Choutas, V., Ghorbani, N., Bolkart, T., Osman, A. A., Tzionas, D., & Black, M. J. (2019). Expressive Body Capture: 3D Hands, Face, and Body from a Single Image. CVPR 2019.

5. Kanazawa, A., Black, M. J., Jacobs, D., & Malik, J. (2018). End-to-end Recovery of Human Shape and Pose. CVPR 2018.

6. Kolotouros, N., Pavlakos, G., Black, M. J., & Daniilidis, K. (2019). Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop. ICCV 2019.

7. Zhang, Y., Chen, X., Li, T., Tian, S., Wang, M., & Tang, S. (2021). PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop. ICCV 2021.

8. Feng, Y., Feng, M., Black, M. J., & Bolkart, T. (2021). Collaborative Regression of Expressive Bodies using Moderation. ICCV 2021.

9. Li, Z., Wu, T., Dellandrea, E., Wang, Y., & Chen, L. (2022). CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation. ECCV 2022.

10. Kocabas, M., Athanasiou, N., & Black, M. J. (2020). VIBE: Video Inference for Human Body Pose and Shape Estimation. CVPR 2020.

11. Ionescu, C., Papava, D., Olaru, V., & Sminchisescu, C. (2014). Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(7), 1325-1339.

12. von Marcard, T., Henschel, R., Black, M., Rosenhahn, B., & Pons-Moll, G. (2018). Recovering Accurate 3D Human Pose in the Wild Using IMUs and a Moving Camera. ECCV 2018.

13. Lassner, C., Romero, J., Kiefel, M., Bogo, F., Black, M. J., & Gehler, P. V. (2017). Unite the People: Closing the Loop Between 3D and 2D Human Representations. CVPR 2017.

14. Patel, P., Huang, C.-H., Tesch, J., Hoffmann, D., Tripathi, S., & Black, M. J. (2021). AGORA: Avatars in Geography Optimized for Regression Analysis. CVPR 2021.

15. Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. (2019). On the Continuity of Rotation Representations in Neural Networks. CVPR 2019.

16. Geman, S., & McClure, D. (1987). Statistical Methods for Tomographic Image Reconstruction. Bulletin of the International Statistical Institute, 52(4), 5-21.

17. Nocedal, J., & Wright, S. J. (2006). Numerical Optimization. Springer.

18. Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005). SCAPE: Shape Completion and Animation of People. ACM Transactions on Graphics (SIGGRAPH), 24(3), 408-416.

19. Johnson, S., & Everingham, M. (2010). Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation. BMVC 2010.

20. Alldieck, T., Magnor, M., Xu, W., Theobalt, C., & Pons-Moll, G. (2019). Learning to Reconstruct People in Clothing from a Single RGB Camera. CVPR 2019.

21. Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2021). OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1), 172-186.

22. Kato, H., Ushiku, Y., & Harada, T. (2018). Neural 3D Mesh Renderer. CVPR 2018.

Lecture 06.1 (Optimization-Based Fitting of SMPL to Images)

1. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M. J. (2015). SMPL: A Skinned Multi-Person Linear Model. ACM Transactions on Graphics, 34(6), 248:1–16.

2. Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., & Black, M. (2016). Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image. ECCV 2016.

3. Hartley, R., & Zisserman, A. (2004). Multiple View Geometry in Computer Vision, 2nd ed. Cambridge University Press.

4. Pavlakos, G., Choutas, V., Ghorbani, N., Bolkart, T., Osman, A. A., Tzionas, D., & Black, M. J. (2019). Expressive Body Capture: 3D Hands, Face, and Body from a Single Image. CVPR 2019.

5. Kanazawa, A., Black, M. J., Jacobs, D., & Malik, J. (2018). End-to-end Recovery of Human Shape and Pose. CVPR 2018.

6. Kolotouros, N., Pavlakos, G., Black, M. J., & Daniilidis, K. (2019). Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop. ICCV 2019.

7. Zhang, H., Tian, Y., Zhou, X., Ouyang, W., Liu, Y., Wang, L., & Sun, Z. (2021). PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop. ICCV 2021.

8. Feng, Y., Feng, M., Black, M. J., & Bolkart, T. (2021). Collaborative Regression of Expressive Bodies using Moderation. ICCV 2021.

9. Li, Z., Wu, T., Dellandrea, E., Wang, Y., & Chen, L. (2022). CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation. ECCV 2022.

10. Kocabas, M., Athanasiou, N., & Black, M. J. (2020). VIBE: Video Inference for Human Body Pose and Shape Estimation. CVPR 2020.

11. Ionescu, C., Papava, D., Olaru, V., & Sminchisescu, C. (2014). Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(7), 1325-1339.

12. von Marcard, T., Henschel, R., Black, M., Rosenhahn, B., & Pons-Moll, G. (2018). Recovering Accurate 3D Human Pose in the Wild Using IMUs and a Moving Camera. ECCV 2018.

13. Lassner, C., Romero, J., Kiefel, M., Bogo, F., Black, M. J., & Gehler, P. V. (2017). Unite the People: Closing the Loop Between 3D and 2D Human Representations. CVPR 2017.

14. Patel, P., Huang, C.-H., Tesch, J., Hoffmann, D., Tripathi, S., & Black, M. J. (2021). AGORA: Avatars in Geography Optimized for Regression Analysis. CVPR 2021.

15. Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. (2019). On the Continuity of Rotation Representations in Neural Networks. CVPR 2019.

16. Geman, S., & McClure, D. (1987). Statistical Methods for Tomographic Image Reconstruction. Bulletin of the International Statistical Institute, 52(4), 5-21.

17. Nocedal, J., & Wright, S. J. (2006). Numerical Optimization. Springer.

18. Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005). SCAPE: Shape Completion and Animation of People. ACM Transactions on Graphics (SIGGRAPH), 24(3), 408-416.

19. Johnson, S., & Everingham, M. (2010). Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation. BMVC 2010.

20. Alldieck, T., Magnor, M., Xu, W., Theobalt, C., & Pons-Moll, G. (2019). Learning to Reconstruct People in Clothing from a Single RGB Camera. CVPR 2019.

21. Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2021). OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1), 172-186.

22. Kato, H., Ushiku, Y., & Harada, T. (2018). Neural 3D Mesh Renderer. CVPR 2018.

Lecture 06.2 (Learning-Based Fitting of SMPL to Images)

1. Kanazawa, A., Black, M. J., Jacobs, D. W., & Malik, J. (2018). End-to-End Recovery of Human Shape and Pose. CVPR 2018.

2. Omran, M., Lassner, C., Pons-Moll, G., Gehler, P., & Schiele, B. (2018). Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation. 3DV 2018.

3. Kolotouros, N., Pavlakos, G., Black, M. J., & Daniilidis, K. (2019). Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop. ICCV 2019.

4. Zhang, H., Tian, Y., Zhou, X., Ouyang, W., Liu, Y., Wang, L., & Sun, Z. (2021). PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop. ICCV 2021.

5. Li, Z., Wu, T., Dellandrea, E., Wang, Y., & Chen, L. (2022). CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation. ECCV 2022.

6. Feng, Y., Feng, M., Black, M. J., & Bolkart, T. (2021). Collaborative Regression of Expressive Bodies using Moderation. ICCV 2021.

7. Kocabas, M., Athanasiou, N., & Black, M. J. (2020). VIBE: Video Inference for Human Body Pose and Shape Estimation. CVPR 2020.

8. Choi, H., Moon, G., Chang, J. Y., & Lee, K. M. (2021). Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video. CVPR 2021.

9. Zhu, W., Ma, X., Wang, Y., Li, H., & Kong, W. (2023). MotionBERT: Unified Pretraining for Human Motion Analysis. ICCV 2023.

10. Pavlakos, G., Zhu, L., Zhou, X., & Daniilidis, K. (2018). Learning to Estimate 3D Human Pose and Shape from a Single Color Image. CVPR 2018.

11. Joo, H., Simon, T., & Sheikh, Y. (2018). Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies. CVPR 2018.

12. Zheng, Z., Yu, T., Wei, Y., Dai, Q., & Liu, Y. (2019). DeepHuman: 3D Human Reconstruction from a Single Image. ICCV 2019.

13. Goel, S., Katan, A., Kanazawa, A., & Malik, J. (2022). Human Mesh Recovery from Multiple Shots. CVPR 2022.

14. Andriluka, M., Pishchulin, L., Gehler, P., & Schiele, B. (2014). 2D Human Pose Estimation: New Benchmark and State of the Art Analysis. CVPR 2014.

15. Kocabas, M., Huang, C.-H. P., Hilliges, O., & Black, M. J. (2021). PARE: Part Attention Regressor for 3D Human Body Estimation. ICCV 2021.

16. Güler, R. A., Neverova, N., & Kokkinos, I. (2018). DensePose: Dense Human Pose Estimation in the Wild. CVPR 2018.

17. Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. (2019). On the Continuity of Rotation Representations in Neural Networks. CVPR 2019.

18. Mehta, D., Sridhar, S., Sotnychenko, O., Rhodin, H., Shafiei, M., Seidel, H.-P., Xu, W., Casas, D., & Theobalt, C. (2017). VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera. ACM Transactions on Graphics, 36(4), 1-14.

19. Mahmood, N., Ghorbani, N., Troje, N. F., Pons-Moll, G., & Black, M. J. (2019). AMASS: Archive of Motion Capture as Surface Shapes. ICCV 2019.

20. Sun, Y., Bao, Q., Liu, W., Fu, Y., Black, M. J., & Mei, T. (2021). Monocular, One-stage, Regression of Multiple 3D People. ICCV 2021.

21. Huang, Z., Zhu, Y., Bogo, F., Lassner, C., Mehta, D., Sotnychenko, O., Romero, J., & Black, M. J. (2022). SMPLer-X: Scaling Up Expressive Human Shape and Pose Modeling. arXiv preprint arXiv:2207.02628.

22. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M. J. (2015). SMPL: A Skinned Multi-Person Linear Model. ACM Transactions on Graphics, 34(6), 248:1–16.

Lecture 07.1 (Fitting SMPL to IMU Optimization)

1. Mahony, R., Hamel, T., & Pflimlin, J.-M. (2008). Nonlinear Complementary Filters on the Special Orthogonal Group. IEEE Transactions on Automatic Control, 53(5), 1203-1218. DOI: https://doi.org/10.1109/TAC.2008.923738 URL: https://hal.archives-ouvertes.fr/hal-00488376/document

2. Madgwick, S. O. H. (2010). An Efficient Orientation Filter for Inertial and Inertial/Magnetic Sensor Arrays. Report x-io Technologies. URL: https://x-io.co.uk/open-source-imu-and-ahrs-algorithms/ GitHub: https://github.com/xioTechnologies/Fusion

3. Roetenberg, D., Luinge, H., & Slycke, P. (2007). Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors. Xsens Technologies White Paper. URL: https://www.xsens.com/hubfs/Downloads/usermanual/MVN_user_manual.pdf

4. Slyper, R., & Hodgins, J. K. (2008). Action Capture with Accelerometers. ACM Symposium on Computer Animation (SCA). DOI: https://doi.org/10.1145/1632592.1632604

5. Tautges, J., Zinke, A., Krüger, B., Weber, A., Baumann, J., & Helten, T. (2011). Motion Reconstruction Using Sparse Accelerometer Data. ACM Transactions on Graphics (TOG), 30(3), Article No. 18. DOI: https://doi.org/10.1145/1966394.1966397

6. Riaz, Q., Tao, G., Krüger, B., & Weber, A. (2015). Motion reconstruction using very few accelerometers and ground contacts. Graphical Models, 79, 23-38. DOI: https://doi.org/10.1016/j.gmod.2015.04.001

7. von Marcard, T., Pons-Moll, G., & Rosenhahn, B. (2017). Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs. Computer Graphics Forum (Eurographics 2017). DOI: https://doi.org/10.1111/cgf.13125 Project Page: https://virtualhumans.mpi-inf.mpg.de/sip/ GitHub: https://github.com/wangsen1312/Sparse-Inertial-Poser (unofficial)

8. Huang, Y., Kaufmann, M., Aksan, E., Black, M. J., & Hilliges, O. (2018). Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time. ACM Transactions on Graphics (SIGGRAPH Asia 2018), 37(6), Article No. 185. DOI: https://doi.org/10.1145/3272127.3275108 arXiv: https://arxiv.org/abs/1809.07116 Project Page & Dataset: http://dip.is.tue.mpg.de/ GitHub: https://github.com/eth-ait/dip18

9. Yi, X., Zhou, Y., Xu, F., Yan, W., & Tan, J. (2021). TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors. ACM Transactions on Graphics (SIGGRAPH Asia), 40(4). DOI: https://doi.org/10.1145/3450626.3459786 arXiv: https://arxiv.org/abs/2105.11796 GitHub: https://github.com/Xinyu-Yi/TransPose

10. von Marcard, T., Henschel, R., Black, M. J., Rosenhahn, B., & Pons-Moll, G. (2018). Recovering Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera. European Conference on Computer Vision (ECCV), 614-631. DOI: https://doi.org/10.1007/978-3-030-01249-6_37 URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Timo_von_Marcard_Recovering_Accurate_3D_ECCV_2018_paper.pdf

11. Trumble, M., Gilbert, A., Malleson, C., Hilton, A., & Collomosse, J. (2017). Total Capture: 3D Human Pose Estimation Fusing Video and Inertial Sensors. British Machine Vision Conference (BMVC), 1-13. DOI: https://doi.org/10.5244/C.31.14 Dataset: https://cvssp.org/data/totalcapture/

12. Mahmood, N., Ghorbani, N., Troje, N. F., Pons-Moll, G., & Black, M. J. (2019). AMASS: Archive of Motion Capture as Surface Shapes. ICCV 2019. DOI: https://doi.org/10.1109/ICCV.2019.00520 Project Page: http://amass.is.tue.mpg.de GitHub: https://github.com/nghorbani/amass

13. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M. J. (2015). SMPL: A Skinned Multi-Person Linear Model. SIGGRAPH Asia 2015. DOI: https://doi.org/10.1145/2816795.2818013 Project Page: https://smpl.is.tue.mpg.de/ GitHub: https://github.com/vchoutas/smplx

14. Kim, J., Bae, S.-H., & Woo, W. (2023). IMUPoser: Full-Body Pose Estimation using IMUs in Phones. CHI Conference on Human Factors in Computing Systems, 1-14. DOI: https://doi.org/10.1145/3544548.3580991 Project Page: https://rikky0611.github.io/IMUPoser/

15. Xu, F., Xu, H., Yin, X., Yi, X., & Tan, J. (2023). PIP: Physics-informed Human Motion Pose Estimation from Sparse Inertial Sensors. IEEE Transactions on Visualization and Computer Graphics. DOI: https://doi.org/10.1109/TVCG.2023.3276484 arXiv: https://arxiv.org/abs/2303.02585

Lecture 07.2 (Fitting SMPL to IMU Learning)

Classic and Optimization-Based Methods

1. Mahony, R., Hamel, T., & Pflimlin, J.-M. (2008). Nonlinear Complementary Filters on the Special Orthogonal Group. IEEE Transactions on Automatic Control, 53(5), 1203-1218. DOI: https://doi.org/10.1109/TAC.2008.923738 URL: https://hal.archives-ouvertes.fr/hal-00488376/document

2. Madgwick, S. O. H. (2010). An Efficient Orientation Filter for Inertial and Inertial/Magnetic Sensor Arrays. Report x-io Technologies. URL: https://x-io.co.uk/open-source-imu-and-ahrs-algorithms/ GitHub: https://github.com/xioTechnologies/Fusion

3. Roetenberg, D., Luinge, H., & Slycke, P. (2007). Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors. Xsens Technologies White Paper. URL: https://www.xsens.com/hubfs/Downloads/usermanual/MVN_user_manual.pdf

4. Slyper, R., & Hodgins, J. K. (2008). Action Capture with Accelerometers. ACM Symposium on Computer Animation (SCA). DOI: https://doi.org/10.1145/1632592.1632604

5. Tautges, J., Zinke, A., Krüger, B., Weber, A., Baumann, J., & Helten, T. (2011). Motion Reconstruction Using Sparse Accelerometer Data. ACM Transactions on Graphics (TOG), 30(3), Article No. 18. DOI: https://doi.org/10.1145/1966394.1966397

6. Riaz, Q., Tao, G., Krüger, B., & Weber, A. (2015). Motion reconstruction using very few accelerometers and ground contacts. Graphical Models, 79, 23-38. DOI: https://doi.org/10.1016/j.gmod.2015.04.001

7. von Marcard, T., Pons-Moll, G., & Rosenhahn, B. (2017). Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs. Computer Graphics Forum (Eurographics 2017). DOI: https://doi.org/10.1111/cgf.13125 Project Page: https://virtualhumans.mpi-inf.mpg.de/sip/ GitHub: https://github.com/wangsen1312/Sparse-Inertial-Poser (unofficial)

Learning-Based Methods

8. Huang, Y., Kaufmann, M., Aksan, E., Black, M. J., & Hilliges, O. (2018). Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time. ACM Transactions on Graphics (SIGGRAPH Asia 2018), 37(6), Article No. 185. DOI: https://doi.org/10.1145/3272127.3275108 arXiv: https://arxiv.org/abs/1809.07116 Project Page & Dataset: http://dip.is.tue.mpg.de/ GitHub: https://github.com/eth-ait/dip18

9. Yi, X., Zhou, Y., Xu, F., Yan, W., & Tan, J. (2021). TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors. ACM Transactions on Graphics (SIGGRAPH Asia), 40(4). DOI: https://doi.org/10.1145/3450626.3459786 arXiv: https://arxiv.org/abs/2105.11796 GitHub: https://github.com/Xinyu-Yi/TransPose

10. Jiang, J., Larsson, P., & Black, M. J. (2022). TIP: Task-Informed Motion Priors for 3D Human Body Tracking. ACM Transactions on Graphics (SIGGRAPH Asia). arXiv: https://arxiv.org/abs/2209.04318 Project Page: https://github.com/jyf588/transformer-inertial-poser

11. Yi, X., Zhou, Y., Xu, F., & Tan, J. (2022). PIP: Physics-informed Human Motion Pose Estimation from Sparse Inertial Sensors. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). DOI: https://doi.org/10.1109/CVPR52688.2022.01322 Project Page: https://xinyu-yi.github.io/PIP/ GitHub: https://github.com/Xinyu-Yi/PIP

12. von Marcard, T., Henschel, R., Black, M. J., Rosenhahn, B., & Pons-Moll, G. (2018). Recovering Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera. European Conference on Computer Vision (ECCV), 614-631. DOI: https://doi.org/10.1007/978-3-030-01249-6_37 URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Timo_von_Marcard_Recovering_Accurate_3D_ECCV_2018_paper.pdf

Datasets and Resources

13. Trumble, M., Gilbert, A., Malleson, C., Hilton, A., & Collomosse, J. (2017). Total Capture: 3D Human Pose Estimation Fusing Video and Inertial Sensors. British Machine Vision Conference (BMVC), 1-13. DOI: https://doi.org/10.5244/C.31.14 Dataset: https://cvssp.org/data/totalcapture/

14. Mahmood, N., Ghorbani, N., Troje, N. F., Pons-Moll, G., & Black, M. J. (2019). AMASS: Archive of Motion Capture as Surface Shapes. ICCV 2019. DOI: https://doi.org/10.1109/ICCV.2019.00520 Project Page: http://amass.is.tue.mpg.de GitHub: https://github.com/nghorbani/amass

15. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M. J. (2015). SMPL: A Skinned Multi-Person Linear Model. SIGGRAPH Asia 2015. DOI: https://doi.org/10.1145/2816795.2818013 Project Page: https://smpl.is.tue.mpg.de/ GitHub: https://github.com/vchoutas/smplx

16. Kim, J., Bae, S.-H., & Woo, W. (2023). IMUPoser: Full-Body Pose Estimation using IMUs in Phones. CHI Conference on Human Factors in Computing Systems, 1-14. DOI: https://doi.org/10.1145/3544548.3580991 Project Page: https://rikky0611.github.io/IMUPoser/

17. Xu, F., Xu, H., Yin, X., Yi, X., & Tan, J. (2023). PIP: Physics-informed Human Motion Pose Estimation from Sparse Inertial Sensors. IEEE Transactions on Visualization and Computer Graphics. DOI: https://doi.org/10.1109/TVCG.2023.3276484 arXiv: https://arxiv.org/abs/2303.02585

Relevant Software and Libraries

18. PyTorch (Deep Learning Framework) URL: https://pytorch.org

19. TensorFlow (Deep Learning Framework) URL: https://www.tensorflow.org

20. PyTorch3D (3D Computer Vision Library) GitHub: https://github.com/facebookresearch/pytorch3d

21. Pinocchio (Rigid Body Dynamics Library) GitHub: https://github.com/stack-of-tasks/pinocchio

22. SMPL-X (Official SMPL Model Implementation) GitHub: https://github.com/vchoutas/smplx

23. SMPLify (Fitting SMPL to Data) GitHub: https://github.com/classner/up/tree/master/up_tools/camera

Lecture 08.1: References for Vertex-Based Clothing Modeling for Virtual Humans

1. Pons-Moll, G., Pujades, S., Hu, S., & Black, M. J. (2017). ClothCap: Seamless 4D Clothing Capture and Retargeting. ACM Transactions on Graphics (Proc. SIGGRAPH Asia 2017), 36(4), Article 73. DOI: https://doi.org/10.1145/3072959.3073711 Project Page: https://clothcap.is.tue.mpg.de/ (Captures detailed clothing and body shape layers from 4D scans; introduces multi-layer mesh registration of garments and body.)

2. Zhang, C., Pujades, S., Black, M. J., & Pons-Moll, G. (2017). Detailed, Accurate, Human Shape Estimation from Clothed 3D Scan Sequences. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017). DOI: https://doi.org/10.1109/CVPR.2017.582 arXiv: https://arxiv.org/abs/1703.04454 Project Page: https://buff.is.tue.mpg.de/ (Introduces the BUFF dataset and an optimization method to estimate naked body shape under clothing by accumulating multi-frame “fusion scans.”)

3. Ma, Q., Yang, J., Ranjan, A., Pujades, S., Pons-Moll, G., Tang, S., & Black, M. J. (2020). Learning to Dress 3D People in Generative Clothing. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). DOI: https://doi.org/10.1109/CVPR42600.2020.00650 arXiv: https://arxiv.org/abs/1907.13615 Project Page: https://cape.is.tue.mpg.de/ GitHub: https://github.com/qianlim/cape_utils (Proposes CAPE, a conditional VAE-GAN clothing model adding a vertex displacement layer to SMPL. CAPE generates realistic pose- and shape-dependent clothing deformations and introduces a large 4D scan dataset for training.)

4. Patel, C., Liao, Z., & Pons-Moll, G. (2020). TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020) – Oral Presentation. DOI: https://doi.org/10.1109/CVPR42600.2020.00739 arXiv: https://arxiv.org/abs/2003.04583 Project Page: https://virtualhumans.mpi-inf.mpg.de/tailornet/ GitHub: https://github.com/chaitanya100100/TailorNet (A neural model that predicts garment-specific vertex displacements on SMPL, conditioned on pose, body shape, and style. It separates high-frequency wrinkle components from low-frequency deformations, achieving fast, realistic clothing animation from a limited physics-simulated training set.)

5. Bhatnagar, B. L., Tiwari, G., Theobalt, C., & Pons-Moll, G. (2019). Multi‐Garment Net: Learning to Dress 3D People from Images. IEEE International Conference on Computer Vision (ICCV 2019), pp. 5419–5429. DOI: https://doi.org/10.1109/ICCV.2019.00552 arXiv: https://arxiv.org/abs/1908.06903 Project Page: https://virtualhumans.mpi-inf.mpg.de/mgn/ GitHub: https://github.com/bharat-b7/MultiGarmentNetwork (Learns separate garment mesh deformations on a SMPL body; uses a “digital wardrobe” of 712 garment templates registered to real scans.)

Body Models and Shape Estimation

6. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M. J. (2015). SMPL: A Skinned Multi-Person Linear Model. ACM Transactions on Graphics (Proc. SIGGRAPH Asia), 34(6), Article 248. DOI: https://doi.org/10.1145/2816795.2818013 Project Page: https://smpl.is.tue.mpg.de/ (The parametric body model underlying most vertex-based clothing methods. SMPL represents the human body as a mesh with shape and pose-dependent deformations.)

7. Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005). SCAPE: Shape Completion and Animation of People. ACM Transactions on Graphics (TOG), 24(3), 408-416. DOI: https://doi.org/10.1145/1073204.1073207 (One of the first data-driven methods for modeling shape and pose deformations of humans.)

8. Bălan, A. O., & Black, M. J. (2008). The Naked Truth: Estimating Body Shape Under Clothing. European Conference on Computer Vision (ECCV 2008), Part II, LNCS 5303, pp. 15-29. DOI: https://doi.org/10.1007/978-3-540-88688-4_2 (Early approach to estimating body shape under clothing using multi-pose optimization.)

Alternative Representations

9. Corona, E., Pumarola, A., Alenyà, G., Pons-Moll, G., & Moreno-Noguer, F. (2021). SMPLicit: Topology-Aware Generative Model for Clothed People. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). DOI: https://doi.org/10.1109/CVPR46437.2021.01170 arXiv: https://arxiv.org/abs/2103.06871 Project Page: http://www.iri.upc.edu/people/ecorona/smplicit/ GitHub: https://github.com/enriccorona/SMPLicit (Generative implicit-surface model that represents clothing of various topologies. SMPLicit uses an unsigned distance field conditioned on SMPL body parameters and latent codes, enabling a single model to generate garments ranging from tees to coats, including multi-layer outfits, with continuous surface detail.)

10. Ma, Q., Yang, J., Tang, S., & Black, M. J. (2021). The Power of Points for Modeling Humans in Clothing. IEEE/CVF International Conference on Computer Vision (ICCV 2021), pp. 10954–10964. DOI: https://doi.org/10.1109/ICCV48922.2021.01079 arXiv: https://arxiv.org/abs/2109.01137 Project Page: https://pop.is.tue.mpg.de/ GitHub: https://github.com/qianlim/POP (Proposes a point-based representation of clothed humans. Instead of deforming mesh vertices, POP learns to model clothing as sets of 3D points attached to the body.)

Datasets

11. BUFF Dataset – 3D scan sequences of 6 subjects (3 male, 3 female) in two outfits performing various motions. Project Page: https://buff.is.tue.mpg.de (Registration required for download)

12. CAPE Dataset – Over 80K 3D scans of 11 subjects in diverse poses and clothing types captured with a 4D body scanner. Project Page: https://cape.is.tue.mpg.de/ (Dataset available for research with registration)

13. TailorNet Synthetic Data – A physics-simulated garment dataset comprising 55,800 garment deformation examples across various poses, body shapes, and styles. Repository: https://github.com/zycliao/TailorNet_dataset

Software and Libraries

14. SMPL Implementation GitHub: https://github.com/vchoutas/smplx

15. Mesh Processing Libraries - Open3D: http://www.open3d.org/ - PyMesh: https://github.com/PyMesh/PyMesh - MeshLab: https://www.meshlab.net/

16. Laplacian Mesh Processing - libigl: https://github.com/libigl/libigl - OpenMesh: https://www.openmesh.org/

17. Physics-Based Cloth Simulation - ARCSim: http://graphics.berkeley.edu/resources/ARCSim/ - Blender Cloth: https://docs.blender.org/manual/en/latest/physics/cloth/index.html

Lecture 09.1: References for Neural Implicit and Point-Based Representations for Clothed Human Modeling

1. Park, J. J., Florence, P., Straub, J., Newcombe, R., & Lovegrove, S. (2019). DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019). DOI: https://doi.org/10.1109/CVPR.2019.00024 arXiv: https://arxiv.org/abs/1901.05103 GitHub: https://github.com/facebookresearch/DeepSDF (Introduced neural networks to represent continuous SDFs for shape generation and completion)

2. Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., & Geiger, A. (2019). Occupancy Networks: Learning 3D Reconstruction in Function Space. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019). DOI: https://doi.org/10.1109/CVPR.2019.00780 arXiv: https://arxiv.org/abs/1812.03828 Project Page: https://autonomousvision.github.io/occupancy_networks/ GitHub: https://github.com/autonomousvision/occupancy_networks (Proposed learning continuous 3D occupancy functions for shape representation, allowing for arbitrary topology and resolution)

3. Corona, E., Pumarola, A., Alenyà, G., Pons-Moll, G., & Moreno-Noguer, F. (2021). SMPLicit: Topology-Aware Generative Model for Clothed People. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). DOI: https://doi.org/10.1109/CVPR46437.2021.01170 arXiv: https://arxiv.org/abs/2103.06871 Project Page: http://www.iri.upc.edu/people/ecorona/smplicit/ GitHub: https://github.com/enriccorona/SMPLicit (Generative implicit-surface model for clothed people that can represent varied garment types with a single model)

4. Deng, B., Lewis, J. P., Jeruzalski, T., Pons-Moll, G., Hinton, G., Norouzi, M., & Tagliasacchi, A. (2020). NASA: Neural Articulated Shape Approximation. European Conference on Computer Vision (ECCV 2020). DOI: https://doi.org/10.1007/978-3-030-58607-2_37 arXiv: https://arxiv.org/abs/1912.03207 Project Page: https://nasa-eccv20.github.io/ (Represented articulated shapes using part-based occupancy fields, one of the first implicit models for posable humans)

5. Chen, X., Zheng, Y., Black, M. J., Hilliges, O., & Geiger, A. (2021). SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes. IEEE/CVF International Conference on Computer Vision (ICCV 2021). DOI: https://doi.org/10.1109/ICCV48922.2021.01139 arXiv: https://arxiv.org/abs/2104.03953 Project Page: https://xuchen-ethz.github.io/snarf/ GitHub: https://github.com/xuchen-ethz/snarf (Introduced forward skinning for implicit shapes, improving pose generalization for neural implicit avatars)

6. Tiwari, G., Bhatnagar, B. L., Tung, T., & Pons-Moll, G. (2021). Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing. IEEE/CVF International Conference on Computer Vision (ICCV 2021). DOI: https://doi.org/10.1109/ICCV48922.2021.00704 arXiv: https://arxiv.org/abs/2108.08807 Project Page: https://virtualhumans.mpi-inf.mpg.de/neuralgif/ GitHub: https://github.com/garvita-tiwari/neuralgif (Presented a factorized approach for animatable clothed humans using backward mapping and learned non-rigid deformations)

7. Saito, S., Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., & Li, H. (2019). PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization. IEEE/CVF International Conference on Computer Vision (ICCV 2019). DOI: https://doi.org/10.1109/ICCV.2019.00257 arXiv: https://arxiv.org/abs/1905.05172 Project Page: https://shunsukesaito.github.io/PIFu/ GitHub: https://github.com/shunsukesaito/PIFu (Single-view reconstruction of clothed humans using pixel-aligned features to predict occupancy)

8. Saito, S., Yang, J., Ma, Q., & Black, M. J. (2021). SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). DOI: https://doi.org/10.1109/CVPR46437.2021.00289 arXiv: https://arxiv.org/abs/2104.03313 Project Page: https://scanimate.is.tue.mpg.de/ (Learned animatable clothed avatars directly from raw scans without explicit correspondences)

9. Saito, S., Simon, T., Saragih, J., & Joo, H. (2020). PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). DOI: https://doi.org/10.1109/CVPR42600.2020.00094 arXiv: https://arxiv.org/abs/2004.00452 Project Page: https://shunsukesaito.github.io/PIFuHD/ GitHub: https://github.com/facebookresearch/pifuhd (High-resolution extension of PIFu with a coarse-to-fine approach)

10. Chen, X., Jiang, T., Song, J., Yang, J., Black, M. J., Hilliges, O., & Tang, S. (2022). imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). DOI: https://doi.org/10.1109/CVPR52688.2022.00354 arXiv: https://arxiv.org/abs/2108.10842 Project Page: https://icon.is.tue.mpg.de/ (Holistic implicit model of the human body including detailed face and fingers)

11. Qian, S., Chang, F., Reijgwart, V., Zhou, Y., Yu, T., Koltun, V., Tagliasacchi, A., & Wei, S. (2022). UNIF: United Neural Implicit Functions for Clothed Human Reconstruction and Animation. European Conference on Computer Vision (ECCV 2022). DOI: https://doi.org/10.1007/978-3-031-20068-7_4 arXiv: https://arxiv.org/abs/2207.03434 GitHub: https://github.com/ShenhanQian/UNIF (Improved part-based implicit models without requiring explicit segmentation)

12. Weng, C., Zhou, B., Tomia, V., Banerjee, S., Seitz, S. M., & Kemelmacher-Shlizerman, I. (2022). HumanNeRF: Free-Viewpoint Rendering of Moving People from Monocular Video. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). DOI: https://doi.org/10.1109/CVPR52688.2022.01430 arXiv: https://arxiv.org/abs/2201.04127 Project Page: https://grail.cs.washington.edu/projects/humannerf/ GitHub: https://github.com/chungyiweng/humannerf (Neural radiance field approach for modeling humans from monocular video)

13. Feng, Y., Yang, Y., Zhao, X., Jiang, Z., Xu, F., Larsen, A. S., & Maniatis, A. (2022). SCARF: Segmented Clothed Avatar Radiance Field. ACM Transactions on Graphics (SIGGRAPH Asia 2022). DOI: https://doi.org/10.1145/3550469.3555408 arXiv: https://arxiv.org/abs/2208.14668 Project Page: https://yfeng95.github.io/scarf/ (Hybrid model combining explicit body mesh with neural radiance field for clothing)

Point-Based Models

14. Ma, Q., Yang, J., Tang, S., & Black, M. J. (2021). The Power of Points for Modeling Humans in Clothing. IEEE/CVF International Conference on Computer Vision (ICCV 2021). DOI: https://doi.org/10.1109/ICCV48922.2021.01079 arXiv: https://arxiv.org/abs/2109.01137 Project Page: https://pop.is.tue.mpg.de/ GitHub: https://github.com/qianlim/POP (Point-based representation of clothed humans with learned features for animation)

15. Qi, C. R., Su, H., Mo, K., & Guibas, L. J. (2017). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2017). DOI: https://doi.org/10.1109/CVPR.2017.16 arXiv: https://arxiv.org/abs/1612.00593 GitHub: https://github.com/charlesq34/pointnet (Pioneering work on deep learning directly on unordered point clouds)

16. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M. J. (2015). SMPL: A Skinned Multi-Person Linear Model. ACM Transactions on Graphics (SIGGRAPH Asia 2015). DOI: https://doi.org/10.1145/2816795.2818013 Project Page: https://smpl.is.tue.mpg.de/ GitHub: https://github.com/vchoutas/smplx (The parametric human body model underlying many clothed human representations)

Datasets and Resources

17. Ma, Q., Yang, J., Ranjan, A., Pujades, S., Pons-Moll, G., Tang, S., & Black, M. J. (2020). Learning to Dress 3D People in Generative Clothing. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). DOI: https://doi.org/10.1109/CVPR42600.2020.00650 arXiv: https://arxiv.org/abs/1907.13615 Project Page and Dataset: https://cape.is.tue.mpg.de/ GitHub: https://github.com/qianlim/cape_utils (Introduced the CAPE dataset: 4D scans of people in various clothing and poses)

18. Bertiche, H., Madadi, M., & Escalera, S. (2020). CLOTH3D: Clothed 3D Humans. European Conference on Computer Vision (ECCV 2020). DOI: https://doi.org/10.1007/978-3-030-58548-8_22 arXiv: https://arxiv.org/abs/2003.12593 Project Page: https://chalearnlap.cvc.uab.cat/dataset/38/description/ (Large-scale synthetic dataset of 3D humans in diverse clothing)

19. RenderPeople Dataset. URL: https://renderpeople.com/ (Commercial dataset of high-quality 3D scans of people in various clothing)

20. Bhatnagar, B. L., Sminchisescu, C., Theobalt, C., & Pons-Moll, G. (2020). Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction. European Conference on Computer Vision (ECCV 2020). DOI: https://doi.org/10.1007/978-3-030-58548-8_19 arXiv: https://arxiv.org/abs/2007.11432 Project Page: https://virtualhumans.mpi-inf.mpg.de/ifnet/ GitHub: https://github.com/bharat-b7/IPNet (Combined parametric models with implicit functions for reconstruction)

Related Software and Libraries

21. Marching Cubes Implementation. GitHub: https://github.com/pmneila/PyMCubes (Python implementation of the Marching Cubes algorithm for extracting isosurfaces from implicit functions)

22. PyTorch3D. GitHub: https://github.com/facebookresearch/pytorch3d (Library for differentiable rendering and 3D operations in deep learning)

23. Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research. GitHub: https://github.com/NVIDIAGameWorks/kaolin (NVIDIA’s library for 3D deep learning operations)

24. Open3D: A Modern Library for 3D Data Processing. GitHub: https://github.com/isl-org/Open3D (Comprehensive library for working with 3D data, including point clouds and mesh processing)

25. SMPL-X (SMPL eXpressive): A Body Model with Separate Articulated Hands and Expressive Face. GitHub: https://github.com/vchoutas/smplx (Extended version of SMPL with detailed face and hand modeling)

Review Papers and Tutorials

26. Alldieck, T., Xu, H., & Sminchisescu, C. (2022). Neural Body Modeling: From Personalized Geometry and Appearance to Animatable Human Models. Invited Paper, Computer Vision and Image Understanding. DOI: https://doi.org/10.1016/j.cviu.2022.103479 arXiv: https://arxiv.org/abs/2207.04213 (Comprehensive survey of neural approaches to human body modeling)

27. Bhatnagar, B. L., Tiwari, G., Theobalt, C., & Pons-Moll, G. (2019). Multi-Garment Net: Learning to Dress 3D People from Images. IEEE/CVF International Conference on Computer Vision (ICCV 2019). DOI: https://doi.org/10.1109/ICCV.2019.00552 arXiv: https://arxiv.org/abs/1908.06903 Project Page: https://virtualhumans.mpi-inf.mpg.de/mgn/ GitHub: https://github.com/bharat-b7/MultiGarmentNetwork (Template-based approach to reconstructing layered clothed humans from images)

28. Xiu, Y., Yang, J., Tzionas, D., & Black, M. J. (2022). ICON: Implicit Clothed Humans Obtained from Normals. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). DOI: https://doi.org/10.1109/CVPR52688.2022.00921 arXiv: https://arxiv.org/abs/2112.09127 Project Page: https://icon.is.tue.mpg.de/ GitHub: https://github.com/YuliangXiu/ICON (Reconstruction of clothed humans from a single image using normal maps and implicit functions)

29. Guo, Y., Wang, Z., Cai, S., Yuan, J., Ding, M., Li, Y., & Wang, H. (2023). DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models. arXiv preprint. arXiv: https://arxiv.org/abs/2304.00916 Project Page: https://dreamavatar.github.io/ (Generative model for creating 3D human avatars from text descriptions)

30. Lee, S.H., Lee, H., Cha, S., Wang, J.M., & Kim, J. (2023). GeoAvatar: Reconstructing Geometrically-Consistent Animatable Avatars from Videos Using 3D Gaussian Splatting. Conference on Neural Information Processing Systems (NeurIPS) Workshops. arXiv: https://arxiv.org/abs/2310.02714 Project Page: https://geoavatar.github.io/ (Multi-person avatar reconstruction using 3D Gaussian splatting)

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Gaussian Splatting

1. Kerbl, B., Kopanas, G., Leimkühler, T., & Drettakis, G. (2023). 3D Gaussian Splatting for Real-Time Radiance Field Rendering. ACM Transactions on Graphics (SIGGRAPH 2023), 42(4). DOI: https://doi.org/10.1145/3592433 GitHub: https://github.com/graphdeco-inria/gaussian-splatting

2. Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. ECCV 2020. DOI: https://doi.org/10.48550/arXiv.2003.08934 GitHub: https://github.com/bmild/nerf

3. Barron, J. T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., & Srinivasan, P. P. (2021). Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields. ICCV 2021. DOI: https://doi.org/10.48550/arXiv.2103.13415 GitHub: https://github.com/google-research/multinerf

4. Barron, J. T., Mildenhall, B., Verbin, D., Srinivasan, P. P., & Hedman, P. (2022). Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields. CVPR 2022. DOI: https://doi.org/10.48550/arXiv.2111.12077 GitHub: https://github.com/google-research/multinerf

5. Müller, T., Evans, A., Schied, C., & Keller, A. (2022). Instant Neural Graphics Primitives with a Multiresolution Hash Encoding. ACM Transactions on Graphics, 41(4). DOI: https://doi.org/10.1145/3528223.3530127 GitHub: https://github.com/NVlabs/instant-ngp

6. Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., & Kanazawa, A. (2022). Plenoxels: Radiance Fields without Neural Networks. CVPR 2022. DOI: https://doi.org/10.48550/arXiv.2112.05131 GitHub: https://github.com/sxyu/svox2

7. Pfister, H., Zwicker, M., van Baar, J., & Gross, M. (2000). Surfels: Surface Elements as Rendering Primitives. SIGGRAPH 2000. DOI: https://doi.org/10.1145/344779.344936 URL: https://www.cs.umd.edu/~zwicker/publications/Surfels-SIG00.pdf

8. Zwicker, M., Pfister, H., van Baar, J., & Gross, M. (2001). Surface Splatting. SIGGRAPH 2001. DOI: https://doi.org/10.1145/383259.383300 URL: https://dl.acm.org/doi/10.1145/383259.383300

9. Wang, Y., Liu, D., Cao, Y., Mu, Z., & Zhang, H. (2019). Differentiable Surface Splatting for Point-based Geometry Processing. ACM Transactions on Graphics, 38(6). DOI: https://doi.org/10.1145/3355089.3356513 GitHub: https://github.com/yifita/DSS

10. Aliev, K. A., Sevastopolsky, A., Kolos, M., Ulyanov, D., & Lempitsky, V. (2020). Neural Point-Based Graphics. ECCV 2020. DOI: https://doi.org/10.1007/978-3-030-58542-6_42 GitHub: https://github.com/alievk/npbg

11. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M. J. (2015). SMPL: A Skinned Multi-Person Linear Model. SIGGRAPH Asia 2015. DOI: https://doi.org/10.1145/2816795.2818013 Project Page: https://smpl.is.tue.mpg.de/

12. Kopanas, G., Philip, J., Leimkühler, T., & Drettakis, G. (2021). Point-Based Neural Rendering with Per-View Optimization. Computer Graphics Forum (Eurographics 2021). DOI: https://doi.org/10.1111/cgf.14339 GitHub: https://repo-sam.inria.fr/fungraph/differentiable-multi-view/

13. Rückert, D., Franke, L., & Stamminger, M. (2022). ADOP: Approximate Differentiable One-Pixel Point Rendering. ACM Transactions on Graphics, 41(4). DOI: https://doi.org/10.1145/3528223.3530122 GitHub: https://github.com/darglein/ADOP

14. Lassner, C., & Zollhöfer, M. (2021). Pulsar: Efficient Sphere-based Neural Rendering. CVPR 2021. DOI: https://doi.org/10.1109/CVPR46437.2021.01086 GitHub: https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/renderer/points/pulsar.py

15. Saito, S., Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., & Li, H. (2019). PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization. ICCV 2019. DOI: https://doi.org/10.1109/ICCV.2019.00257 GitHub: https://github.com/shunsukesaito/PIFu

16. Saito, S., Simon, T., Saragih, J., & Joo, H. (2020). PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization. CVPR 2020. DOI: https://doi.org/10.1109/CVPR42600.2020.00207 GitHub: https://github.com/facebookresearch/pifuhd

17. Xiu, Y., Yang, J., Tzionas, D., & Black, M. J. (2022). ICON: Implicit Clothed humans Obtained from Normals. CVPR 2022. DOI: https://doi.org/10.1109/CVPR52688.2022.00401 GitHub: https://github.com/YuliangXiu/ICON

18. Snavely, N., Seitz, S. M., & Szeliski, R. (2006). Photo Tourism: Exploring Photo Collections in 3D. SIGGRAPH 2006. DOI: https://doi.org/10.1145/1141911.1141964 Project Page: https://phototour.cs.washington.edu/

19. Mahmood, N., Ghorbani, N., Troje, N. F., Pons-Moll, G., & Black, M. J. (2019). AMASS: Archive of Motion Capture as Surface Shapes. ICCV 2019. DOI: https://doi.org/10.1109/ICCV.2019.00520 Project Page: http://amass.is.tue.mpg.de

20. Wu, G., Yi, T., Fang, J., Xie, L., Zhang, X., Wei, W., Liu, W., Tian, Q., & Wang, X. (2024). 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering. CVPR 2024. DOI: https://doi.org/10.48550/arXiv.2310.08579 GitHub: https://github.com/hustvl/4DGaussians

21. Levoy, M., & Hanrahan, P. (1996). Light Field Rendering. SIGGRAPH 1996. DOI: https://doi.org/10.1145/237170.237199 URL: https://graphics.stanford.edu/papers/light/

22. Gortler, S. J., Grzeszczuk, R., Szeliski, R., & Cohen, M. F. (1996). The Lumigraph. SIGGRAPH 1996. DOI: https://doi.org/10.1145/237170.237200 URL: https://www.microsoft.com/en-us/research/publication/the-lumigraph/

23. Bhatnagar, B. L., Tiwari, G., Theobalt, C., & Pons-Moll, G. (2019). Multi-Garment Net: Learning to Dress 3D People from Images. ICCV 2019. DOI: https://doi.org/10.1109/ICCV.2019.00543 GitHub: https://github.com/bharat-b7/MultiGarmentNetwork

24. Yao, K., Wu, M., Dai, H., Tuytelaars, T., & Yu, J. (2025). BG-Triangle: Bézier Gaussian Triangle for 3D Vectorization and Rendering. arXiv:2503.13961.

25. Wang, Z., Kanamori, Y., & Endo, Y. (2024). EG-HumanNeRF: Efficient Generalizable Human NeRF Utilizing Human Prior for Sparse View. arXiv:2410.12242.

26. Zheng, S., Zhou, B., Shao, R., Liu, B., Zhang, S., Nie, L., & Liu, Y. (2024). GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis. CVPR 2024 (Highlight).

27. Kwon, Y., Fang, B., Lu, Y., Dong, H., Zhang, C., Carrasco, F. V., Mosella-Montoro, A., Xu, J., Takagi, S., Kim, D., Prakash, A., & De la Torre, F. (2024). Generalizable Human Gaussians for Sparse View Synthesis. arXiv:2407.12777.

28. Yuan, Y., Shen, Q., Yang, X., & Wang, X. (2025). 1000+ FPS 4D Gaussian Splatting for Dynamic Scene Rendering. arXiv:2503.16422.

29. Zhang, X., Liu, Z., Ge, X., He, D., Xu, T., Lin, Z., Yan, S., & Zhang, J. (2024). MEGA: Memory-Efficient 4D Gaussian Splatting for Dynamic Scenes. arXiv:2309.17367 (ICLR 2025 submission).

30. Huang, B., Yu, Z., Chen, A., Geiger, A., & Gao, S. (2024). 2D Gaussian Splatting for Geometrically Accurate Radiance Fields. ACM SIGGRAPH 2024.

31. Li, Z., Zheng, Z., Wang, L., & Liu, Y. (2024). Animatable Gaussians: Learning Pose-Dependent Gaussian Maps for High-Fidelity Human Avatar Modeling. CVPR 2024.

32. RenderPeople – URL: https://www.renderpeople.com

33. Knapitsch, A., Park, J., Zhou, Q.-Y., & Koltun, V. (2017). Tanks and Temples: Benchmarking Large-Scale Scene Reconstruction. ACM Transactions on Graphics, 36(4), 1–13. DOI: https://doi.org/10.1145/3072959.3073599 Dataset: https://tanksandtemples.org/