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About this paper
Yuan-Ming Li, Qize Yang, Nan Lei, Shenghao Fu, Ling-An Zeng, Jian-Fang Hu, Xihan Wei, Wei-Shi Zheng
Researchers introduce IRG-MotionLLM, a model that integrates motion generation, assessment, and refinement through an iterative text-motion dialogue for text-to-motion tasks. This approach enables the model to natively self-correct and iteratively improve generated motions, achieving improved alignment with complex textual descriptions compared to prior methods.
Key insights
- Explicitly integrating motion assessment and refinement tasks serves as a critical intermediary, effectively bridging the knowledge gap between motion understanding and generation to enhance text-motion alignment.
- A structured Chain-of-Thought template, combined with an 'Ignore Incorrect' strategy during training, enables MoLLMs to natively perform iterative self-correction, significantly improving motion fidelity to text.
- Reinforcement learning is effective in unleashing multi-round interleaved reasoning potential, prompting the model to explore longer and more effective refinement trajectories, even from partially misaligned initial motions.
Summary by alphaXiv.
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