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|a 10.1109/TVCG.2025.3588509
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|a eng
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|a Wang, Yin
|e verfasserin
|4 aut
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| 245 |
1 |
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|a MOST
|b Motion Diffusion Model for Rare Text via Temporal Clip Banzhaf Interaction
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|c 2025
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|a Text
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|a ƒaComputermedien
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|a Date Revised 05.09.2025
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a 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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|a Journal Article
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|a Li, Mu
|e verfasserin
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1 |
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|a Leng, Zhiying
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Frederick W B
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liang, Xiaohui
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 31(2025), 10 vom: 13. Sept., Seite 8994-9007
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|g year:2025
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|g month:09
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