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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2603.01000 |
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| _version_ | 1866910051976871936 |
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| author | Li, Yuze Gong, Dong Cao, Xiao Yuan, Junchao Li, Dongsheng Zhou, Lei Koh, Yun Sing Yan, Cheng Zhang, Xinyu |
| author_facet | Li, Yuze Gong, Dong Cao, Xiao Yuan, Junchao Li, Dongsheng Zhou, Lei Koh, Yun Sing Yan, Cheng Zhang, Xinyu |
| contents | Motion transfer has emerged as a promising direction for controllable video generation, yet existing methods largely focus on single-object scenarios and struggle when multiple objects require distinct motion patterns. In this work, we present FlexiMMT, the first implicit image-to-video (I2V) motion transfer framework that explicitly enables multi-object, multi-motion transfer. Given a static multi-object image and multiple reference videos, FlexiMMT independently extracts motion representations and accurately assigns them to different objects, supporting flexible recombination and arbitrary motion-to-object mappings. To address the core challenge of cross-object motion entanglement, we introduce a Motion Decoupled Mask Attention Mechanism that uses object-specific masks to constrain attention, ensuring that motion and text tokens only influence their designated regions. We further propose a Differentiated Mask Propagation Mechanism that derives object-specific masks directly from diffusion attention and progressively propagates them across frames efficiently. Extensive experiments demonstrate that FlexiMMT achieves precise, compositional, and state-of-the-art performance in I2V-based multi-object multi-motion transfer. Our project page is: https://ethan-li123.github.io/FlexiMMT_page/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01000 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Let Your Image Move with Your Motion! -- Implicit Multi-Object Multi-Motion Transfer Li, Yuze Gong, Dong Cao, Xiao Yuan, Junchao Li, Dongsheng Zhou, Lei Koh, Yun Sing Yan, Cheng Zhang, Xinyu Computer Vision and Pattern Recognition Motion transfer has emerged as a promising direction for controllable video generation, yet existing methods largely focus on single-object scenarios and struggle when multiple objects require distinct motion patterns. In this work, we present FlexiMMT, the first implicit image-to-video (I2V) motion transfer framework that explicitly enables multi-object, multi-motion transfer. Given a static multi-object image and multiple reference videos, FlexiMMT independently extracts motion representations and accurately assigns them to different objects, supporting flexible recombination and arbitrary motion-to-object mappings. To address the core challenge of cross-object motion entanglement, we introduce a Motion Decoupled Mask Attention Mechanism that uses object-specific masks to constrain attention, ensuring that motion and text tokens only influence their designated regions. We further propose a Differentiated Mask Propagation Mechanism that derives object-specific masks directly from diffusion attention and progressively propagates them across frames efficiently. Extensive experiments demonstrate that FlexiMMT achieves precise, compositional, and state-of-the-art performance in I2V-based multi-object multi-motion transfer. Our project page is: https://ethan-li123.github.io/FlexiMMT_page/ |
| title | Let Your Image Move with Your Motion! -- Implicit Multi-Object Multi-Motion Transfer |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.01000 |