Reasoning Physical Video Generation with Diffusion Timestep Tokens via Reinforcement Learning
This addresses the problem of physical inconsistency in video generation for applications requiring realistic simulations, though it appears incremental by building on existing diffusion and reasoning methods.
The paper tackled the challenge of generating videos that adhere to physical laws by integrating symbolic reasoning and reinforcement learning, resulting in a framework that produces physically consistent videos as demonstrated experimentally.
Despite recent progress in video generation, producing videos that adhere to physical laws remains a significant challenge. Traditional diffusion-based methods struggle to extrapolate to unseen physical conditions (eg, velocity) due to their reliance on data-driven approximations. To address this, we propose to integrate symbolic reasoning and reinforcement learning to enforce physical consistency in video generation. We first introduce the Diffusion Timestep Tokenizer (DDT), which learns discrete, recursive visual tokens by recovering visual attributes lost during the diffusion process. The recursive visual tokens enable symbolic reasoning by a large language model. Based on it, we propose the Phys-AR framework, which consists of two stages: The first stage uses supervised fine-tuning to transfer symbolic knowledge, while the second stage applies reinforcement learning to optimize the model's reasoning abilities through reward functions based on physical conditions. Our approach allows the model to dynamically adjust and improve the physical properties of generated videos, ensuring adherence to physical laws. Experimental results demonstrate that PhysAR can generate videos that are physically consistent.