MotionShot: Adaptive Motion Transfer across Arbitrary Objects for Text-to-Video Generation
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911069761437696 |
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| author | Liu, Yanchen Sun, Yanan Xing, Zhening Gao, Junyao Chen, Kai Pei, Wenjie |
| author_facet | Liu, Yanchen Sun, Yanan Xing, Zhening Gao, Junyao Chen, Kai Pei, Wenjie |
| contents | Existing text-to-video methods struggle to transfer motion smoothly from a reference object to a target object with significant differences in appearance or structure between them. To address this challenge, we introduce MotionShot, a training-free framework capable of parsing reference-target correspondences in a fine-grained manner, thereby achieving high-fidelity motion transfer while preserving coherence in appearance. To be specific, MotionShot first performs semantic feature matching to ensure high-level alignments between the reference and target objects. It then further establishes low-level morphological alignments through reference-to-target shape retargeting. By encoding motion with temporal attention, our MotionShot can coherently transfer motion across objects, even in the presence of significant appearance and structure disparities, demonstrated by extensive experiments. The project page is available at: https://motionshot.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16310 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MotionShot: Adaptive Motion Transfer across Arbitrary Objects for Text-to-Video Generation Liu, Yanchen Sun, Yanan Xing, Zhening Gao, Junyao Chen, Kai Pei, Wenjie Computer Vision and Pattern Recognition Existing text-to-video methods struggle to transfer motion smoothly from a reference object to a target object with significant differences in appearance or structure between them. To address this challenge, we introduce MotionShot, a training-free framework capable of parsing reference-target correspondences in a fine-grained manner, thereby achieving high-fidelity motion transfer while preserving coherence in appearance. To be specific, MotionShot first performs semantic feature matching to ensure high-level alignments between the reference and target objects. It then further establishes low-level morphological alignments through reference-to-target shape retargeting. By encoding motion with temporal attention, our MotionShot can coherently transfer motion across objects, even in the presence of significant appearance and structure disparities, demonstrated by extensive experiments. The project page is available at: https://motionshot.github.io/. |
| title | MotionShot: Adaptive Motion Transfer across Arbitrary Objects for Text-to-Video Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.16310 |