DC-Motion: Decoupling Structure and Details via Discrete-Continuous Tokens for Human Motion Generation
Text-to-motion generation requires modeling both global action structure and fine-grained motion dynamics from natural language. Existing approaches typically rely on either continuous diffusion models or vector-quantized discrete representations. Diffusion models generate smooth motions but lack explicit compositional structure for temporal planning, while discrete token-based methods improve controllability but compress motion into finite codebooks, losing fine-grained dynamics. We argue that this limitation stems from a representation mismatch: action semantics such as intent, phase transitions, and temporal layout are inherently discrete and compositional, whereas joint trajectories a...
| Authors | Hequan Wang, Xuean Chen, Jiaxu Zhang et al. |
|---|---|
| Primary category | cs.GR |
| Published | 28 May 2026 |
| Last updated at source | 6 July 2026 |
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