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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2511.17844 |
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| _version_ | 1866908946769379328 |
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| author | Cheng, Shihan Kulkarni, Nilesh Hyde, David Smirnov, Dmitriy |
| author_facet | Cheng, Shihan Kulkarni, Nilesh Hyde, David Smirnov, Dmitriy |
| contents | Fine-tuning large-scale text-to-video diffusion models to add new generative controls, such as those over physical camera parameters (e.g., shutter speed or aperture), typically requires vast, high-fidelity datasets that are difficult to acquire. In this work, we propose a data-efficient fine-tuning strategy that learns these controls from sparse, low-quality synthetic data. We show that not only does fine-tuning on such simple data enable the desired controls, it actually yields superior results to models fine-tuned on photorealistic "real" data. Beyond demonstrating these results, we provide a framework that justifies this phenomenon both intuitively and quantitatively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17844 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Less is More: Data-Efficient Adaptation for Controllable Text-to-Video Generation Cheng, Shihan Kulkarni, Nilesh Hyde, David Smirnov, Dmitriy Computer Vision and Pattern Recognition Artificial Intelligence 68U05 I.3.3; I.5.4 Fine-tuning large-scale text-to-video diffusion models to add new generative controls, such as those over physical camera parameters (e.g., shutter speed or aperture), typically requires vast, high-fidelity datasets that are difficult to acquire. In this work, we propose a data-efficient fine-tuning strategy that learns these controls from sparse, low-quality synthetic data. We show that not only does fine-tuning on such simple data enable the desired controls, it actually yields superior results to models fine-tuned on photorealistic "real" data. Beyond demonstrating these results, we provide a framework that justifies this phenomenon both intuitively and quantitatively. |
| title | Less is More: Data-Efficient Adaptation for Controllable Text-to-Video Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68U05 I.3.3; I.5.4 |
| url | https://arxiv.org/abs/2511.17844 |