Skeletal-Driven Animation of Anatomical Humans via Neural Deformation Gradients

dc.contributor.authorNolte, Gerrit
dc.contributor.authorKemper, Fabian
dc.contributor.authorSchwanecke, Ulrich
dc.contributor.authorBotsch, Mario
dc.contributor.editorMasia, Belen
dc.contributor.editorThies, Justus
dc.date.accessioned2026-04-17T13:52:50Z
dc.date.available2026-04-17T13:52:50Z
dc.date.issued2026
dc.description.abstractMost 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.
dc.description.number2
dc.description.sectionheadersAnimating Humans with Gestures and Style
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume45
dc.identifier.doi10.1111/cgf.70388
dc.identifier.issn1467-8659
dc.identifier.pages14 pages
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70388
dc.identifier.urihttps://doi.org/10.1111/cgf70388
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.
dc.rightsCC-BY-4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectComputing methodologies
dc.subjectPhysical simulation
dc.subjectNeural networks
dc.subjectVolumetric models
dc.titleSkeletal-Driven Animation of Anatomical Humans via Neural Deformation Gradients
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