Han, G., Liang, M., Tang, J., Cheng, Y., Liu, W., & Huang, S. (2024). ReinDiffuse: Crafting Physically Plausible Motions with Reinforced Diffusion Model. arXiv preprint arXiv:2410.07296.
This paper introduces ReinDiffuse, a novel method for generating physically plausible human motion sequences from textual descriptions by combining motion diffusion models with reinforcement learning. The research aims to address the limitations of existing text-to-motion generation models, which often produce physically unrealistic movements due to their inability to fully incorporate real-world physics.
ReinDiffuse adapts Motion Diffusion Models (MDM) to be compatible with reinforcement learning by reparameterizing their output into a parameterized distribution of actions. This allows for the application of reinforcement learning techniques, specifically Proximal Policy Optimization (PPO), to optimize the model's policy for generating physically plausible motions. The researchers designed a reward function that focuses on penalizing four common non-physical behaviors: sliding steps, floating, ground penetration, and foot clipping.
Experiments on HumanML3D and KIT-ML datasets demonstrate that ReinDiffuse significantly outperforms state-of-the-art models in terms of physical plausibility and motion quality. Notably, ReinDiffuse achieves a 29% improvement in FID on HumanML3D and a 34% improvement on KIT-ML compared to the baseline MDM. The generated motions effectively mitigate common physical issues like floating, penetration, foot clipping, and skating, demonstrating the effectiveness of the reinforcement learning approach in capturing physical commonsense.
ReinDiffuse offers a novel and effective approach to generate physically plausible human motions from text descriptions. By combining the strengths of motion diffusion models and reinforcement learning, the method overcomes the limitations of existing approaches that rely on computationally expensive physics simulations or struggle to fully capture the nuances of real-world physics.
This research contributes significantly to the field of computer vision, particularly in the area of text-to-motion generation. The proposed method has the potential to advance applications in various domains, including animation, gaming, virtual reality, and robotics, by enabling the creation of more realistic and believable human character movements.
The study acknowledges limitations in the need to design specific reward functions for each physical problem, which can be labor-intensive. Additionally, the current implementation relies on joint locations for reward calculation, potentially overlooking subtle physical issues that might arise in mesh-based representations. Future research could explore incorporating mesh-based physical rewards and investigating the use of semantically related rewards to further enhance the model's capabilities.
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