Text-to-motion generation has advanced rapidly, but most current models are built for single-shot output. If a user is unhappy with a specific movement or detail, the typical remedy is to regenerate the entire sequence, which is inefficient and frustrating. A new arXiv paper, “Learn to Modify: Self-evolving Multi-agent Refinement Framework for Conversational Text-to-Motion Generation,” directly targets this limitation.
The authors propose a framework that treats motion generation as a conversational, iterative process. Instead of starting over, a team of specialized agents works together to modify the existing motion based on user feedback. The framework is described as self-evolving, meaning it learns from each refinement interaction to become better at making targeted changes.
This shift from single-shot generation to iterative refinement could make text-to-motion tools more practical for real-world use, where users often need precise control over output. The paper is available on arXiv, though details on the specific agent architecture and evaluation results are not included in the abstract.