AI RESEARCH
Encoder-Free Human Motion Understanding via Structured Motion Descriptions
arXiv CS.CV
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ArXi:2604.21668v1 Announce Type: new The world knowledge and reasoning capabilities of text-based large language models (LLMs) are advancing rapidly, yet current approaches to human motion understanding, including motion question answering and captioning, have not fully exploited these capabilities. Existing LLM-based methods typically learn motion-language alignment through dedicated encoders that project motion features into the LLM's embedding space, remaining constrained by cross-modal representation and alignment.