.. _ref-section: 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_references: 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: ``_ URL: ``_ 2. Madgwick, S. O. H. (2010). *An Efficient Orientation Filter for Inertial and Inertial/Magnetic Sensor Arrays*. Report x-io Technologies. URL: ``_ GitHub: ``_ 3. Roetenberg, D., Luinge, H., & Slycke, P. (2007). *Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors*. Xsens Technologies White Paper. URL: ``_ 4. Slyper, R., & Hodgins, J. K. (2008). *Action Capture with Accelerometers*. ACM Symposium on Computer Animation (SCA). DOI: ``_ 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: ``_ 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: ``_ 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: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page & Dataset: ``_ GitHub: ``_ 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: ``_ arXiv: ``_ GitHub: ``_ 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: ``_ URL: ``_ 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: ``_ Dataset: ``_ 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: ``_ Project Page: ``_ GitHub: ``_ 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: ``_ Project Page: ``_ GitHub: ``_ 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: ``_ Project Page: ``_ 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: ``_ arXiv: ``_ 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: ``_ URL: ``_ 2. Madgwick, S. O. H. (2010). *An Efficient Orientation Filter for Inertial and Inertial/Magnetic Sensor Arrays*. Report x-io Technologies. URL: ``_ GitHub: ``_ 3. Roetenberg, D., Luinge, H., & Slycke, P. (2007). *Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors*. Xsens Technologies White Paper. URL: ``_ 4. Slyper, R., & Hodgins, J. K. (2008). *Action Capture with Accelerometers*. ACM Symposium on Computer Animation (SCA). DOI: ``_ 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: ``_ 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: ``_ 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: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page & Dataset: ``_ GitHub: ``_ 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: ``_ arXiv: ``_ GitHub: ``_ 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: ``_ Project Page: ``_ 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: ``_ Project Page: ``_ GitHub: ``_ 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: ``_ URL: ``_ 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: ``_ Dataset: ``_ 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: ``_ Project Page: ``_ GitHub: ``_ 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: ``_ Project Page: ``_ GitHub: ``_ 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: ``_ Project Page: ``_ 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: ``_ arXiv: ``_ Relevant Software and Libraries ----------------------------------- 18. PyTorch (Deep Learning Framework) URL: ``_ 19. TensorFlow (Deep Learning Framework) URL: ``_ 20. PyTorch3D (3D Computer Vision Library) GitHub: ``_ 21. Pinocchio (Rigid Body Dynamics Library) GitHub: ``_ 22. SMPL-X (Official SMPL Model Implementation) GitHub: ``_ 23. SMPLify (Fitting SMPL to Data) GitHub: ``_ 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: ``_ Project Page: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ Project Page: ``_ (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: ``_ (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: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ (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: ``_ (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: ``_ Software and Libraries ------------------------ 14. SMPL Implementation GitHub: ``_ 15. Mesh Processing Libraries - Open3D: ``_ - PyMesh: ``_ - MeshLab: ``_ 16. Laplacian Mesh Processing - libigl: ``_ - OpenMesh: ``_ 17. Physics-Based Cloth Simulation - ARCSim: ``_ - Blender Cloth: ``_ 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: ``_ arXiv: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ (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: ``_ arXiv: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ GitHub: ``_ (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: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page and Dataset: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ (Large-scale synthetic dataset of 3D humans in diverse clothing) 19. RenderPeople Dataset. URL: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ GitHub: ``_ (Combined parametric models with implicit functions for reconstruction) Related Software and Libraries ------------------------------------ 21. Marching Cubes Implementation. GitHub: ``_ (Python implementation of the Marching Cubes algorithm for extracting isosurfaces from implicit functions) 22. PyTorch3D. GitHub: ``_ (Library for differentiable rendering and 3D operations in deep learning) 23. Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research. GitHub: ``_ (NVIDIA's library for 3D deep learning operations) 24. Open3D: A Modern Library for 3D Data Processing. GitHub: ``_ (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: ``_ (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: ``_ arXiv: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ GitHub: ``_ (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: ``_ arXiv: ``_ Project Page: ``_ GitHub: ``_ (Reconstruction of clothed humans from a single image using normal maps and implicit functions) 29. 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