Motion Attribution for Video Generation
Despite the rapid progress of video generation models, the role of data in influencing motion is poorly understood. We present Motive (MOTIon attribution for Video gEneration), a motion-centric, gradient-based data attribution framework that scales to modern, large, high-quality video datasets and models. We use this to study which fine-tuning clips improve or degrade temporal dynamics. Motive isolates temporal dynamics from static appearance via motion-weighted loss masks, yielding efficient and scalable motion-specific influence computation. On text-to-video models, Motive identifies clips that strongly affect motion and guides data curation that improves temporal consistency and physical plausibility. With Motive-selected high-influence data, our method improves both motion smoothness and dynamic degree on VBench, achieving a 74.1% human preference win rate compared with the pretrained base model. To our knowledge, this is the first framework to attribute motion rather than visual appearance in video generative models and to use it to curate fine-tuning data.
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Xindi Wu
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2026-01-14T12:54:39.179Z
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Xindi Wu
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Despoina Paschalidou
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2026-01-14T12:54:45.681Z
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Despoina Paschalidou
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user
dpaschalidou
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Jun Gao
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Antonio Torralba
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2026-01-14T12:54:55.870Z
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Antonio
Antoniotorralbaborruel
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Antoniotorralbaborruel
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Laura Leal-Taixé
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Olga Russakovsky
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Sanja Fidler
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Jonathan Lorraine
admin_assigned
2026-01-14T12:55:12.587Z
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Jonathan Lorraine
lorraine2
user
lorraine2
Motion Attribution for Video Generation
Despite the rapid progress of video generation models, the role of data in influencing motion is poorly understood. We present Motive (MOTIon attribution for Video gEneration), a motion-centric, gradient-based data attribution framework that scales to modern, large, high-quality video datasets and models. We use this to study which fine-tuning clips improve or degrade temporal dynamics. Motive isolates temporal dynamics from static appearance via motion-weighted loss masks, yielding efficient and scalable motion-specific influence computation. On text-to-video models, Motive identifies clips that strongly affect motion and guides data curation that improves temporal consistency and physical plausibility. With Motive-selected high-influence data, our method improves both motion smoothness and dynamic degree on VBench, achieving a 74.1% human preference win rate compared with the pretrained base model. To our knowledge, this is the first framework to attribute motion rather than visual appearance in video generative models and to use it to curate fine-tuning data.
Motive is a gradient-based data attribution framework that identifies influential video clips for motion improvement in text-to-video models through motion-weighted loss masking.
video generation models data attribution gradient-based motion-centric temporal dynamics motion-weighted loss masks fine-tuning text-to-video models VBench motion smoothness dynamic degree