PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation
Shaowei Liu, Zhongzheng Ren, Saurabh Gupta, Shenlong Wang

TL;DR
PhysGen introduces a physics-grounded image-to-video generation framework that combines physical simulation with generative models to produce realistic, controllable, and physically plausible videos from a single image and user input.
Contribution
The paper presents a novel integration of model-based physics simulation with data-driven video generation for improved realism and control in image-to-video synthesis.
Findings
PhysGen outperforms existing methods in realism and physical plausibility.
The system enables user-controlled dynamic interactions in generated videos.
Quantitative and user studies confirm superior performance over prior approaches.
Abstract
We present PhysGen, a novel image-to-video generation method that converts a single image and an input condition (e.g., force and torque applied to an object in the image) to produce a realistic, physically plausible, and temporally consistent video. Our key insight is to integrate model-based physical simulation with a data-driven video generation process, enabling plausible image-space dynamics. At the heart of our system are three core components: (i) an image understanding module that effectively captures the geometry, materials, and physical parameters of the image; (ii) an image-space dynamics simulation model that utilizes rigid-body physics and inferred parameters to simulate realistic behaviors; and (iii) an image-based rendering and refinement module that leverages generative video diffusion to produce realistic video footage featuring the simulated motion. The resulting…
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Taxonomy
TopicsHuman Pose and Action Recognition · Computer Graphics and Visualization Techniques · Generative Adversarial Networks and Image Synthesis
MethodsDiffusion
