Neural Voxel Dynamics: Learning Volumetric Feature Advection for 3D Physics in V-JEPA Latent Space
We present Neural Voxel Dynamics, a self-supervised framework for learning 3D latent dynamics from monocular video. While generative video models now produce visually compelling motion, their predominantly 2D representations provide limited geometric structure for modeling and controlling physical interactions. Instead, we learn dynamics in a lifted volumetric latent space. Specifically, we unproject semantic Video Joint-Embedding Predictive Architecture (V-JEPA) features into a voxel grid using monocular depth priors, producing a geometrically grounded representation that retains rich video features. We then introduce Volumetric Feature Advection, an action-conditioned transition model t...
| Authors | Zican Wang, Niloy Mitra |
|---|---|
| Primary category | cs.CV |
| Published | 24 June 2026 |
| Last updated at source | 8 October 2026 |
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