PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models
Physical principles are fundamental to realistic visual simulation, but remain a significant oversight in transformer-based video generation. This gap highlights a critical limitation in rendering rigid body motion, a core tenet of classical mechanics. While computer graphics and physics-based simulators can easily model such collisions using Newton formulas, modern pretrain-finetune paradigms discard the concept of object rigidity during pixel-level global denoising. Even perfectly correct mathematical constraints are treated as suboptimal solutions (i.e., conditions) during model optimization in post-training, fundamentally limiting the physical realism of generated videos. Motivated by these considerations, we introduce, for the first time, a physics-aware reinforcement learning paradigm for video generation models that enforces physical collision rules directly in high-dimensional spaces, ensuring the physics knowledge is strictly applied rather than treated as conditions. Subsequently, we extend this paradigm to a unified framework, termed Mimicry-Discovery Cycle (MDcycle), which allows substantial fine-tuning while fully preserving the model's ability to leverage physics-grounded feedback. To validate our approach, we construct new benchmark PhysRVGBench and perform extensive qualitative and quantitative experiments to thoroughly assess its effectiveness.
696da79a3f1837bfb89709ea
Qiyuan Zhang
696da79a3f1837bfb89709eb
Biao Gong
admin_assigned
2026-01-19T14:43:37.154Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/644fcbea4f7316588267dc80/w8-2Gkaw9BN9VzppNXrTP.jpeg
Biao Gong
BiaoGong
user
BiaoGong
696da79a3f1837bfb89709ec
Shuai Tan
admin_assigned
2026-01-19T14:43:45.871Z
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Shuai Tan
Shuaishuai0219
user
Shuaishuai0219
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Zheng Zhang
696da79a3f1837bfb89709ee
Yujun Shen
admin_assigned
2026-01-19T14:43:52.528Z
6969f153bb68e272ac6e2676
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Yujun Shen
shen12313
user
shen12313
696da79a3f1837bfb89709ef
Xing Zhu
696da79a3f1837bfb89709f0
Yuyuan Li
696da79a3f1837bfb89709f1
Kelu Yao
696da79a3f1837bfb89709f2
Chunhua Shen
696da79a3f1837bfb89709f3
Changqing Zou
PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models
Physical principles are fundamental to realistic visual simulation, but remain a significant oversight in transformer-based video generation. This gap highlights a critical limitation in rendering rigid body motion, a core tenet of classical mechanics. While computer graphics and physics-based simulators can easily model such collisions using Newton formulas, modern pretrain-finetune paradigms discard the concept of object rigidity during pixel-level global denoising. Even perfectly correct mathematical constraints are treated as suboptimal solutions (i.e., conditions) during model optimization in post-training, fundamentally limiting the physical realism of generated videos. Motivated by these considerations, we introduce, for the first time, a physics-aware reinforcement learning paradigm for video generation models that enforces physical collision rules directly in high-dimensional spaces, ensuring the physics knowledge is strictly applied rather than treated as conditions. Subsequently, we extend this paradigm to a unified framework, termed Mimicry-Discovery Cycle (MDcycle), which allows substantial fine-tuning while fully preserving the model's ability to leverage physics-grounded feedback. To validate our approach, we construct new benchmark PhysRVGBench and perform extensive qualitative and quantitative experiments to thoroughly assess its effectiveness.
A physics-aware reinforcement learning paradigm is introduced for video generation that enforces physical collision rules directly in high-dimensional spaces, ensuring strict application of physics knowledge rather than treating it as conditional constraints.
reinforcement learning video generation physical collision rules high-dimensional spaces physics-aware Mimicry-Discovery Cycle PhysRVGBench