Skeletal‐Driven Animation of Anatomical Humans via Neural Deformation Gradients

Abstract Most real‐time animation techniques for digital humans are limited to deforming the outer skin surface. Geometric skinning methods are highly efficient but struggle with artifacts such as collapsing joints or self‐intersections when animating inner anatomy along with the outer skin. Volumetric physics‐based simulations, on the other hand, naturally resolve these issues by coordinating bones, muscles, and skin, but are far too slow for interactive use. We solve this problem by training a neural network to predict deformation gradients. Learning deformation gradients instead of vertex displacements makes our method naturally robust to artifacts such as element inversion or volume deviation. Our model, trained on high‐quality finite element simulations, generalizes well across diverse body shapes and poses. This enables anatomically consistent and physically grounded animation of bones, muscles, and skin at interactive frame rates.