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Geometric skinning methods are highly efficient but struggle with artifacts such as collapsing joints or self\u2010intersections when animating inner anatomy along with the outer skin. Volumetric physics\u2010based simulations, on the other hand, naturally resolve these issues by coordinating bones, muscles, and skin, but are far too slow for interactive use.<\/jats:p>\n                  <jats:p>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\u2010quality finite element simulations, generalizes well across diverse body shapes and poses. 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