MOST: Motion Diffusion Model for Rare Text via Temporal Clip Banzhaf Interaction
Journal:
arXiv
Published Date:
Jul 9, 2025
Abstract
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.
