Fine-grained Human Motion Understanding with Language Models
Fine-grained motion understanding requires knowing not only which poses occur, but also when they occur. Existing motion-language models either compress motion clips or concatenate pose features without retaining the timing of each pose, making action duration and temporal scale ambiguous under temporal subsampling. We introduce FiGMo, an LLM-based model that represents motion as timestamp-grounded skeletal poses, supporting reasoning over action order and duration. A unified pose encoder applies this representation to both 2D and 3D skeletons, including poses detected from real videos, while RGB can optionally provide contextual information. To reason over timestamped pose sequences, the...
| Authors | Thomas Markhorst, Zhi-Yi Lin, Jouh Yeong Chew et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 June 2026 |
| Last updated at source | 15 October 2026 |
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