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arXiv · 2511.17844

Less is More: Data-Efficient Adaptation for Controllable Text-to-Video Generation

Shihan Cheng·Nilesh Kulkarni·David Hyde·Dmitriy Smirnov
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Abstract

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.

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Shihan Cheng, Nilesh Kulkarni, David Hyde, Dmitriy Smirnov. 2025-11-21. Less is More: Data-Efficient Adaptation for Controllable Text-to-Video Generation. https://arxiv.org/abs/2511.17844

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