← 研踪 PaperTrailCVF · CVPR 2026:cvf:8a2cdab5266bf9dd8e2c976483879b33 · 记录 #1
Less is More: Data-Efficient Adaptation for Controllable Text-to-Video Generation
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-01 23:50
摘要
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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记录 #1 · 2026 · 最新观测版本
Less is More: Data-Efficient Adaptation for Controllable Text-to-Video Generation
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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