PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models

Physical principles are fundamental to realistic visual sim-ulation, but remain a significant oversight in video generation. Thisgap highlights a critical limitation in rendering rigid body motion, acore tenet of classical mechanics. While computer graphics and physics-based simulators can easily model such dynamics using Newton for-mulas, modern video generative models discard the concept of objectrigidity during pixel-level global denoising. Existing methods attemptto tackle this problem through physical data augmentation, dynamicspre-simulation, or reinforcement learning with VLM ratings, but noneof these approaches accurately reflect physical principles or enable themodel to internalize physical knowledge. Motivated by these considera-tions, we introduce reinforcement learning with physically verifiable re-wards. We design a quantitatively verifiable metric that combines Trajec-tory Offset and Collision Detection, which can accurately capture rigid-body motion states and assess the quality of generated samples. Subse-quently, we extend this paradigm to a unified post-training framework,termed Mimicry-Discovery Cycle, which enables stable training onout-of-distribution scenarios while improving overall model performance.To validate our approach, we construct new benchmark PhysRVGBenchand perform extensive qualitative and quantitative experiments to thor-oughly assess its effectiveness. The code and demo can be found athttps://lucaria-academy.github.io/PhysRVG/.