MotionShot: Adaptive Motion Transfer across Arbitrary Objects for Text-to-Video Generation Article Swipe

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Yanchen Liu , Yanan Sun , Zhening Xing , Junyao Gao , Kai Chen , Wenjie Pei ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2507.16310

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/.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2507.16310
PDF: https://arxiv.org/pdf/2507.16310
OA Status: green
OpenAlex ID: https://openalex.org/W4414417554

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OpenAlex ID: https://openalex.org/W4414417554

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DOI: https://doi.org/10.48550/arxiv.2507.16310

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Title: MotionShot: Adaptive Motion Transfer across Arbitrary Objects for Text-to-Video Generation

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-07-22

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Authors: Yanchen Liu, Yanan Sun, Zhening Xing, Junyao Gao, Kai Chen, Wenjie Pei

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Landing page: https://arxiv.org/abs/2507.16310

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PDF URL: https://arxiv.org/pdf/2507.16310

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Open access: Yes

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OA status: green

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OA URL: https://arxiv.org/pdf/2507.16310

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Cited by: 0

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