PhysLDM applies latent diffusion to deformable mesh simulation
Original title: PhysLDM: Latent Diffusion for High-Fidelity Deformable Simulation
A spatiotemporal VAE reports about 2.48 mm reconstruction error at up to 78x token compression.
PhysLDM is a latent-diffusion method for one-shot simulation of high-resolution deformable volumetric meshes. A spatiotemporal VAE is designed to avoid staircase artifacts of standard temporal compression. The authors find diffusion models chaotic dynamics better than deterministic regression, which tends to produce non-physical averages. A model trained kinematically on an Objaverse-scale set generalizes zero-shot to unseen datasets and supports differentiable inverse design.
Unconfirmed: The claim of being the first such paradigm is the authors' assessment.
- Reconstruction precision is about 2.48 mm on meter-scale scenes.
- Token compression reaches up to 78x.
- Diffusion is preferred over regression for chaotic deformable dynamics.
- The model is trained purely kinematically and is differentiable.
From the original article
1 min read at huggingface.co
Neural simulation of high-fidelity deformable bodies is a foundational challenge in computer graphics and physical AI. Long-horizon prediction for high-resolution 3D volumetric meshes is hard: autoregressive methods are prone to error accumulation, while direct multi-frame prediction at native resolution is computationally prohibitive.
By Yu Zhang, Xudong Xu, Xingang Pan · huggingface.co
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