PhysGM: Large Physical Gaussian Model for Feed-Forward 4D Synthesis
This work addresses the challenge of efficient and realistic physics-based motion synthesis for applications in computer graphics and simulation, representing a novel approach but with incremental improvements in speed and integration.
The paper tackles the problem of physics-grounded 4D synthesis from a single image by introducing PhysGM, a feed-forward framework that jointly predicts 3D Gaussian representations and physical properties, enabling high-fidelity 4D rendering in one minute with a significant speedup over prior methods.
While physics-grounded 3D motion synthesis has seen significant progress, current methods face critical limitations. They typically rely on pre-reconstructed 3D Gaussian Splatting (3DGS) representations, while physics integration depends on either inflexible, manually defined physical attributes or unstable, optimization-heavy guidance from video models. To overcome these challenges, we introduce PhysGM, a feed-forward framework that jointly predicts a 3D Gaussian representation and its physical properties from a single image, enabling immediate, physical simulation and high-fidelity 4D rendering. We first establish a base model by jointly optimizing for Gaussian reconstruction and probabilistic physics prediction. The model is then refined with physically plausible reference videos to enhance both rendering fidelity and physics prediction accuracy. We adopt the Direct Preference Optimization (DPO) to align its simulations with reference videos, circumventing Score Distillation Sampling (SDS) optimization which needs back-propagating gradients through the complex differentiable simulation and rasterization. To facilitate the training, we introduce a new dataset PhysAssets of over 24,000 3D assets, annotated with physical properties and corresponding guiding videos. Experimental results demonstrate that our method effectively generates high-fidelity 4D simulations from a single image in one minute. This represents a significant speedup over prior works while delivering realistic rendering results. Our project page is at:https://hihixiaolv.github.io/PhysGM.github.io/