Presents the first self-supervised neural garment dynamics model that generates persistent wrinkles by converting learning into a moving energy minimization problem with gradual transition from elastic to elasto-plastic target materials.
In: ACM SIGGRAPH 2024 conference papers
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
PoseShield learns a neural collision field directly in SMPL pose space with Eikonal regularization to correct self-collisions post-hoc in human pose estimation and motion generation, achieving 95.8% success on a new benchmark.
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Self-supervised Garment Dynamics with Persistent Wrinkles
Presents the first self-supervised neural garment dynamics model that generates persistent wrinkles by converting learning into a moving energy minimization problem with gradual transition from elastic to elasto-plastic target materials.
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PoseShield: Neural Collision Fields for Human Self-Collision Resolution
PoseShield learns a neural collision field directly in SMPL pose space with Eikonal regularization to correct self-collisions post-hoc in human pose estimation and motion generation, achieving 95.8% success on a new benchmark.