VeloGauss: Learning Physically Consistent Gaussian Velocity Fields from Videos
In this paper, we aim to jointly model the geometry, appearance, and physical information of 3D scenes solely from dynamic multi-view videos, without relying on any physical priors. Existing works typically employ physical losses merely as soft constraints or integrate physical simulations into neural networks; however, these approaches often fail to effectively learn complex motion physics. Although modeling velocity fields holds the potential to capture authentic physical information, due to the lack of appropriate physical constraints, current methods are unable to correctly learn the interaction mechanisms between rigid and non-rigid particles. To address this, we propose VeloGauss, d...
| Authors | Nengbo Lu, Bin Zhao |
|---|---|
| Primary category | cs.CV |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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