.. _lecture_01_3_intro_human_models_overview: Lecture 01.3 – Introduction to Human Models (Overview) ====================================================== .. raw:: html `Lecture Slides: Introduction to Human Models `_ This lecture presents a comprehensive overview of human body modeling, from historical roots to state-of-the-art techniques. We explore how knowledge from anatomy, computer vision, computer graphics, and biomechanics converges to create digital representations of human shape, motion, and behavior. ------------------------------------------------------------- 1. Historical Context ------------------------------------------------------------- Human body modeling has evolved through centuries of scientific investigation: Early Scientific Studies ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Weber Brothers (1836)**: Conducted one of the first quantitative gait analyses, measuring timing and distances in human walking. - **Marey and Muybridge (1870s-1880s)**: Pioneered sequential photography (chronophotography) to capture and analyze human motion. - **Braune and Fischer (1890s)**: Applied Newtonian mechanics to study body-segment motion, calculating joint forces and energy expenditure during locomotion. Mid-20th Century to Digital Era ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Biomechanical Research**: Rehabilitation needs for World War II veterans spurred comprehensive gait studies at the University of California in the 1950s. - **Computer Graphics (1970s-1980s)**: - Phong's illumination model (1975) improved rendering of 3D surfaces - Fred Parke (1972) created the first 3D facial models - **Motion Capture Development**: - Tom Calvert's goniometer suit (1983) for medical motion capture - Marker-based optical systems emerged in the late 1980s - Vicon systems with reflective markers became standard in the 1990s 21st Century Advances ^^^^^^^^^^^^^^^^^^^^^^^^^ - **Markerless Motion Capture**: - Hogg's work (1983) demonstrated tracking walking figures from video - Multi-camera systems in the 2000s enabled visual hull reconstruction - Depth sensors (Microsoft Kinect, 2010) accelerated markerless capture - **Deep Learning Revolution**: - Convolutional networks for 2D and 3D pose estimation (OpenPose, DeepPose) - Parametric body models like SMPL enabled single-image 3D reconstruction - **Behavior Synthesis**: - From keyframe animation and physical simulations (1980s-1990s) - To motion graphs for recombining captured clips (2000s) - Modern deep learning approaches for generating realistic movements Today's human body models combine anatomical insight, physics, and data-driven learning to achieve unprecedented realism and functionality. ------------------------------------------------------------- 2. Mathematical Foundations ------------------------------------------------------------- Parametric Body Models ^^^^^^^^^^^^^^^^^^^^^^^^^^ The Skinned Multi-Person Linear (SMPL) model exemplifies modern parametric approaches: .. math:: M(\boldsymbol{\theta}, \boldsymbol{\beta}) : \mathbb{R}^{|\theta| + |\beta|} \rightarrow \mathbb{R}^{3N} where: - :math:`\boldsymbol{\theta}` represents pose parameters (joint angles, typically 72 parameters for 24 joints) - :math:`\boldsymbol{\beta}` represents shape parameters (typically 10 principal components) - :math:`N` is the number of mesh vertices (6890 in SMPL) SMPL can be factored into: 1. **Base mesh** (mean shape) 2. **Shape blend shapes** (scaled by :math:`\boldsymbol{\beta}`) 3. **Pose blend shapes** (dependent on :math:`\boldsymbol{\theta}`) 4. **Skeleton-driven deformation** via linear blend skinning This creates a differentiable, low-dimensional representation that can be efficiently optimized. Implicit Surface Representations ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Alternative to meshes, implicit functions define the body as a level set: .. math:: \text{Surface} = \{\mathbf{x} \in \mathbb{R}^3 : f(\mathbf{x}) = 0\} Common implicit representations include: - **Signed Distance Functions (SDFs)**: :math:`f(\mathbf{x})` gives distance to surface (positive outside, negative inside) - **Occupancy Functions**: Binary inside/outside classification Neural networks can approximate these functions: - **DeepSDF**: MLPs outputting distance values for query points - **Neural Articulated Shape Approximation (NASA)**: Implicit functions conditioned on pose Kinematic Modeling ^^^^^^^^^^^^^^^^^^^^^^ Human movement is modeled as an articulated figure: - **Forward Kinematics (FK)**: Computing limb positions from joint angles - Global transform of joint :math:`j`: :math:`G_j = G_{\text{parent}(j)} \cdot \text{Trans}(L_{\text{parent}(j)}) \cdot R_j(\theta_j)` - **Inverse Kinematics (IK)**: Solving for joint angles given desired end-effector positions - Often uses Jacobian :math:`J(\boldsymbol{\theta}) = \frac{\partial \mathbf{p}}{\partial \boldsymbol{\theta}}` relating joint angle changes to end-effector position changes - **Skinning**: Vertex position :math:`v_i'` is computed as :math:`v_i' = \sum_j w_{ij} (\mathbf{T}_j(\theta) \cdot v_i)` where :math:`w_{ij}` are skinning weights For pose and shape estimation, optimization seeks parameters that minimize the distance between model and observations, often using iterative methods or learning-based approaches. ------------------------------------------------------------- 3. Image Formation and Rendering ------------------------------------------------------------- Projecting 3D humans to 2D images involves several processes: Camera Models ^^^^^^^^^^^^^^^^^ The pinhole camera model provides the foundation: .. math:: (u, v) = \left(f \frac{X}{Z} + c_x, f \frac{Y}{Z} + c_y\right) where: - :math:`(X, Y, Z)` are 3D coordinates in camera space - :math:`f` is focal length - :math:`(c_x, c_y)` is the principal point Camera extrinsic parameters (rotation :math:`R`, translation :math:`t`) transform world coordinates to camera coordinates before projection. Shading and Visibility ^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Lambertian shading**: Surface brightness proportional to :math:`I = \rho \, (\mathbf{n} \cdot \mathbf{l})` where :math:`\mathbf{n}` is surface normal and :math:`\mathbf{l}` is light direction - **Phong model**: Adds specular highlights for more realistic rendering - **Z-buffer**: Resolves visibility by keeping only the nearest surface at each pixel - **Silhouettes**: In multi-view setups, combining silhouettes creates visual hulls approximating the 3D volume of a person Differentiable Rendering ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Recent advances make the rendering process differentiable, enabling gradient-based optimization: - **Softened rasterization**: Allows gradients to flow even through discrete operations - **End-to-end optimization**: Neural networks can be trained to predict body parameters by comparing rendered projections with input images - **Self-supervised learning**: Using image synthesis error as a loss when 3D ground truth is unavailable This capability allows fitting 3D human models to 2D observations by iteratively refining the model to align with the input image. ------------------------------------------------------------- 4. Surface Representation Methods ------------------------------------------------------------- Two dominant approaches represent human body geometry: Explicit Mesh Models ^^^^^^^^^^^^^^^^^^^^^^^^ - **Fixed topology**: Surface represented by vertices connected in a consistent mesh structure (e.g., SMPL with 6890 vertices and ~13,776 triangular faces) - **Blendshapes**: Shape variations expressed as vertex displacements from a template mesh - SMPL uses linear combinations of learned shape basis vectors - **Advantages**: - Efficient rendering on graphics hardware - Direct semantic correspondence across shapes - Simple animation via skinning - Easy texture mapping and collision detection - **Limitations**: - Cannot handle topology changes - Fixed resolution (more details require more vertices) Implicit Function Models ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Continuous field**: Body defined as level set of a function in 3D space - Neural networks can approximate these fields (e.g., DeepSDF, NASA) - **Advantages**: - Topological flexibility (can represent open jackets, loose clothing) - Arbitrary resolution (can be sampled at any density) - Natural handling of complex geometry - Continuous surfaces and gradients - **Limitations**: - Computationally expensive to render - Harder to animate in real-time - Less direct control for artists Hybrid approaches combine explicit models for coarse structure with implicit functions for high-resolution details. ------------------------------------------------------------- 5. Motion Capture and Behavior Synthesis ------------------------------------------------------------- Capturing Human Motion ^^^^^^^^^^^^^^^^^^^^^^^^^^ **Marker-Based Systems**: - **Optical motion capture**: Reflective markers tracked by infrared cameras - **Inertial systems**: IMUs measuring orientation and acceleration on each limb - **Advantages**: High accuracy, temporal resolution - **Limitations**: Requires specialized equipment, markers can interfere with natural movement **Markerless Approaches**: - **Multi-camera systems**: Reconstruct visual hulls from silhouettes - **Deep learning**: Models like OpenPose detect 2D keypoints from regular video - **Model-fitting**: SMPLify optimizes 3D body model to match 2D detections - **End-to-end networks**: HMR, VIBE directly regress SMPL parameters from images/video **Sparse Sensing**: - Recent work shows as few as 5 IMUs can reconstruct full body pose - Learning fills gaps in sparse observations using motion priors Behavior Synthesis ^^^^^^^^^^^^^^^^^^^^^^ **Motion Graphs and Clip-Based Methods**: - Stitch existing motion clips at compatible transitions - Introduced by Kovar et al. (2002) - Good for interactive control with available motion data **Physics-Based Simulation**: - Model body as articulated rigid bodies with physics - Apply joint torques to generate movement - Examples include Hodgins et al. (1995) simulating athletic movements **Deep Learning Approaches**: - **Generative models**: VAEs, GANs, diffusion models learn motion distributions - Can be conditioned on music, action labels, or other high-level inputs - Example: DeepMimic (Peng et al. 2018) uses reinforcement learning to imitate mocap clips **Hybrid Methods**: - Combine data-driven motion with physics constraints - Xie et al. (2021) incorporate physics into training from video data - Ensure plausible dynamics while leveraging large datasets ------------------------------------------------------------- 6. Clothing Modeling ------------------------------------------------------------- Realistic virtual humans require clothing that moves naturally: Physically-Based Simulation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Mass-spring systems**: Cloth as mesh with physical forces - **Finite element methods**: More accurate but computationally expensive - **Baraff & Witkin (1998)**: Pioneered efficient implicit integration for cloth .. math:: E = \text{Elastic forces} + \text{Gravity} + \text{Collision response} - **Advantages**: Realistic dynamics for any movement - **Limitations**: Computationally intensive, requires accurate material parameters Data-Driven Approaches ^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Garment shape spaces**: Learn how clothing deforms with different poses - **TailorNet**: Neural network predicting clothing deformation from body pose and shape - **Displacement models**: Map offsets from body surface to clothing - **Advantages**: Fast runtime performance after training - **Limitations**: Limited to training distribution of poses/shapes Implicit Clothing Models ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Neural implicit functions**: Represent clothing as level sets - **BCNet**: Two-layer model with body and cloth as separate implicit surfaces - **Advantages**: Handle topology changes (open jackets, loose garments) - **Limitations**: More complex to train and render Layered approaches combine body models with separate clothing models, enabling transfer between different bodies while maintaining natural movement. ------------------------------------------------------------- 7. Human-Object Interaction ------------------------------------------------------------- Modeling interactions between humans and their environment: Physics-Based Methods ^^^^^^^^^^^^^^^^^^^^^^^^^ - **Contact constraints**: Ensure no penetration, appropriate reaction forces - **Motion planning**: Find trajectories that accomplish tasks while obeying physics - **Contact-Invariant Optimization**: Mordatch et al. (2012) optimized motion with contact variables - **Applications**: Sitting, climbing, manipulating objects with proper physics Learning-Based Approaches ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Affordances**: Learn which objects allow which actions (chairs afford sitting) - **PROX**: Hassan et al. (2019) captured realistic human-scene interactions - **Pose prediction**: Generate appropriate human poses near specific objects - **Applications**: Scene population, interaction prediction, ergonomic assessment Hybrid Systems ^^^^^^^^^^^^^^^^^^ - **Reinforce learning for tasks**: Learn to sit (ICLR 2020) used neural policies for chair interactions - **COUCH (2021)**: Combined data-driven pose synthesis with controllable contact points - **Applications**: Interactive virtual humans that respond naturally to environments Human-object interaction modeling is crucial for virtual reality, robotics, and digital human simulations that involve realistic environmental interaction. ------------------------------------------------------------- 8. Applications ------------------------------------------------------------- Virtual human models power applications across numerous domains: Entertainment and Media ^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Film and Animation**: Digital characters and crowds in movies - **Video Games**: Real-time character control and procedural animation - **Virtual Reality**: Avatars representing users in immersive environments Healthcare and Biomechanics ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Gait Analysis**: Quantify walking patterns for diagnosis and treatment - **Rehabilitation**: Track and assess patient movements during therapy - **Surgical Planning**: Patient-specific anatomical models - **Sports Performance**: Technique analysis and injury prevention Engineering and Design ^^^^^^^^^^^^^^^^^^^^^^^^^ - **Ergonomics**: Design workspaces and products for human comfort - **Robotics**: Human-robot interaction and collaborative environments - **Autonomous Systems**: Pedestrian tracking and behavior prediction Human-Computer Interaction ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Gesture Recognition**: Body-based input for interfaces - **Virtual Try-On**: Visualize clothing on personalized avatars - **Accessibility**: Design interfaces for diverse body types and abilities Scientific Research ^^^^^^^^^^^^^^^^^^^^^^ - **Psychology**: Study body language and non-verbal communication - **Anthropology**: Analyze human movement across cultures - **Forensics**: Reconstruct accidents or crime scenes ------------------------------------------------------------- 9. Challenges and Future Directions ------------------------------------------------------------- Despite significant progress, several challenges remain: Scalability and Generalization ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Population Diversity**: Current models often lack coverage of children, elderly, or unusual body types - **Motion Diversity**: Rare or extreme actions may fall outside training distributions - **Computational Efficiency**: High-fidelity models require significant resources Higher-Fidelity Dynamics ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Soft Tissue**: Modeling fat and muscle jiggling during movement - **Fine Details**: Realistic facial expressions and hand articulation - **Secondary Motion**: Cloth, hair, and accessories with physical accuracy Data and Labeling Constraints ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Ground Truth**: Difficult to obtain accurate 3D pose for in-the-wild data - **Contact Information**: Precisely capturing where and how bodies interact with objects - **Privacy Concerns**: Ethical use of motion data that may be identifying Physics and Learning Integration ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Physical Plausibility**: Learned models can produce physically impossible results - **Differentiable Physics**: Backpropagating through simulations for training - **Simulation-to-Real Gap**: Ensuring models transfer from simulation to real data Semantic and Cognitive Aspects ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Action Planning**: High-level decision making for autonomous virtual humans - **Social Behavior**: Modeling gestures, personal space, and interaction norms - **Context Awareness**: Understanding environmental constraints and affordances Realism vs. Controllability ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - **Multi-Level Control**: Balancing high-level commands with low-level physics - **Real-Time Performance**: Maintaining realism under interactive constraints - **Artist Tools**: Providing intuitive interfaces for animation and control The future likely holds unified models combining shape, motion, clothing, and intention in a single framework, enabling applications from immersive telepresence to autonomous digital humans that interact naturally with users.