Less is More: Data-Efficient Adaptation for Controllable Text-to-Video Generation
Shihan Cheng, Nilesh Kulkarni, David Hyde, Dmitriy Smirnov
2026Year
Abstract
Figure 1. Our "Less is More" framework for data-efficient controllable generation. A T2V backbone, fine-tuned solely on a sparse, low-fidelity synthetic dataset (left), learns to generalize to complex physical controls. This enables precise, high-fidelity manipulation of shutter speed (motion blur), aperture (bokeh), and color temperature during real-world inference (right), driven by a continuous control.
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- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 35,902 citations
- LoRA: Low-Rank Adaptation of Large Language ModelsEdward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu et al.ICLR 2022 · 18,833 citations
- Adding Conditional Control to Text-to-Image Diffusion ModelsLvmin Zhang, Anyi Rao, Maneesh AgrawalaICCV 2023 · 6,759 citations
- Scalable Diffusion Models with TransformersWilliam Peebles, Saining XieICCV 2023 · 5,568 citations
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