探索文本与运动对齐以实现文本驱动的运动生成
Exploring Motion-Language Alignment for Text-driven Motion Generation
AI总结：
本文提出MLA-Gen框架，通过整合全局运动先验与局部条件，提升文本驱动运动生成的对齐精度，并引入SinkRatio指标解决注意力集中问题，提升运动质量和语义对齐。
摘要：
本文提出MLA-Gen框架，通过整合全局运动先验与局部条件，提升文本驱动运动生成的对齐精度，并引入SinkRatio指标解决注意力集中问题，提升运动质量和语义对齐。
英文摘要：
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 disproportionately concentrates on the start text token, limiting the utilization of informative textual cues and leading to degraded semantic grounding. To analyze this issue, we introduce SinkRatio, a metric for measuring attention concentration, and develop alignment-aware masking and control strategies to regulate attention during generation. Extensive experiments demonstrate that our approach consistently improves both motion quality and motion-language alignment over strong baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
