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About this paper
Yiming Huang, Weilin Wan, Yue Yang, Chris Callison-Burch, Mark Yatskar, Lingjie Liu
CoMo introduces a framework for controllable human motion generation and editing by decomposing 3D motion into semantically interpretable "pose codes." It leverages Large Language Models (LLMs) to directly manipulate these codes based on natural language instructions, enabling fine-grained and consistent edits to existing motion sequences.
Key insights
- Explicitly factorizing motion into semantically meaningful pose codes allows LLMs to directly reason about and manipulate motion, enabling fine-grained control that was previously difficult to achieve.
- Directly editing pose code sequences with LLMs preserves the original motion's characteristics while applying precise changes, overcoming the consistency issues of full motion regeneration.
- Augmenting high-level text descriptions with LLM-generated fine-grained keywords significantly enhances the generator's ability to capture subtle motion details and improve text-motion consistency.
Summary by alphaXiv.
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