Exploring Motion-Language Alignment for Text-driven Motion Generation
Text-driven human motion generation aims to synthesize realistic motion sequences that follow textual descriptions. Despite recent advances, accurately aligning motion dynamics with textual semantics remains a fundamental challenge. In this paper, we revisit text-to-motion generation from the perspective of motion-language alignment and propose MLA-Gen, a framework that integrates global motion priors with fine-grained local conditioning. This design enables the model to capture common motion patterns, while establishing detailed alignment between texts and motions. Furthermore, we identify a previously overlooked attention sink phenomenon in human motion generation, where attention dispr...
| Authors | Ruxi Gu, Zilei Wang, Wei Wang |
|---|---|
| Primary category | cs.CV |
| Published | 3 April 2026 |
| Last updated at source | 10 October 2026 |
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