Large text-to-video models trained on internet-scale data have demonstrated exceptional capabilities in generating high-fidelity videos from arbitrary textual descriptions. However, adapting these models to tasks with limited domain-specific data, such as animation or robotics videos, poses a significant computational challenge, since finetuning a pretrained large model can be prohibitively expensive. Inspired by how a small modifiable component (e.g., prompts, prefix-tuning) can adapt a large language model to perform new tasks without requiring access to the model weights, we investigate how to adapt a large pretrained text-to-video model to a variety of downstream domains and tasks without finetuning. In answering this question, we propose Video Adapter, which leverages the score function of a large pretrained video diffusion model as a probabilistic prior to guide the generation of a task-specific small video model. Our experiments show that Video Adapter is capable of incorporating the broad knowledge and preserving the high fidelity of a large pretrained video model in a task-specific small video model that is able to generate high-quality yet specialized videos on a variety of tasks such as animation, egocentric modeling, and modeling of simulated and real-world robotics data. More videos can be found on the website https://video-adapter.github.io/.

探索如何适应大规模预训练的文本到视频模型到多个下游领域和任务而无需微调的问题，我们提出了 Video Adapter，它利用了大型预训练视频扩散模型的分数函数作为概率先验来指导任务特定的小型视频模型的生成，实验结果表明 Video Adapter 能够将大型预训练视频模型的广泛知识和高保真度融合到能够在动画、自我建模和模拟以及真实世界机器人数据建模等多种任务中生成高质量且专业的视频。

文本到视频模型的概率适应