3D Morphable Models

3D morphable models encode variation in shape, identity, expression, and pose in a shared parameterized representation, making 3D reconstruction and animation easier to control. Common approaches fit models such as FLAME or SMPL-X to images or scans using learned regression, geometric constraints, dense correspondences, or nonlinear optimization; synthetic data and anatomical or visibility-aware priors help with difficult views and clothing. These representations support facial reconstruction, portrait animation, body fitting, and category-level object correspondence, with performance assessed on scan-fitting, reconstruction, and tracking benchmarks. A central challenge is balancing geometric accuracy and realistic detail with robust generalization across viewpoints, demographics, and appearances, while keeping model parameters useful for downstream tasks.