用细粒度语义匹配生成罕见文本对应的自然动作
MOST: Motion Diffusion Model for Rare Text via Temporal Clip Banzhaf Interaction
- 通过时间片段的巴赞法交互，精准衡量文本与动作片段的语义一致性
- 在罕见提示下实现当前最优的动作检索与生成效果
- 适合需要精准控制动作细节的动画生成场景
我们提出MOST，一种基于时间片段巴赞法交互的运动扩散模型，旨在解决从稀有语言提示生成人体动作的长期挑战。先前方法因运动冗余导致粗粒度匹配和关键语义线索遗漏。我们的核心洞察是利用细粒度片段关系缓解这些问题。MOST的检索阶段首次提出时间片段巴赞法交互，精确量化文本-动作在片段层面的语义一致性和匹配度，实现直接、细粒度的文本-动作片段对齐，消除普遍存在的冗余。生成阶段通过运动提示模块有效利用检索到的动作片段，生成语义一致的动作序列。大量评估表明，MOST在文本到动作的检索与生成任务中达到当前最优性能，定量与定性结果均验证其有效性，尤其在稀有提示下表现突出。
原文摘要 · Abstract (English)
We introduce MOST, a novel motion diffusion model via temporal clip Banzhaf interaction, aimed at addressing the persistent challenge of generating human motion from rare language prompts. While previous approaches struggle with coarse-grained matching and overlook important semantic cues due to motion redundancy, our key insight lies in leveraging fine-grained clip relationships to mitigate these issues. MOST's retrieval stage presents the first formulation of its kind - temporal clip Banzhaf interaction - which precisely quantifies textual-motion coherence at the clip level. This facilitates direct, fine-grained text-to-motion clip matching and eliminates prevalent redundancy. In the generation stage, a motion prompt module effectively utilizes retrieved motion clips to produce semantically consistent movements. Extensive evaluations confirm that MOST achieves state-of-the-art text-to-motion retrieval and generation performance by comprehensively addressing previous challenges, as demonstrated through quantitative and qualitative results highlighting its effectiveness, especially for rare prompts.
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