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Large text-to-video models trained on internet-scale data have demonstratedexceptional capabilities in generating high-fidelity videos from arbitrarytextual descriptions. However, adapting these models to tasks with limiteddomain-specific data, such as animation or robotics videos, poses a significantcomputational challenge, since finetuning a pretrained large model can beprohibitively expensive. Inspired by how a small modifiable component (e.g.,prompts, prefix-tuning) can adapt a large language model to perform new taskswithout requiring access to the model weights, we investigate how to adapt alarge pretrained text-to-video model to a variety of downstream domains andtasks without finetuning. In answering this question, we propose Video Adapter,which leverages the score function of a large pretrained video diffusion modelas a probabilistic prior to guide the generation of a task-specific small videomodel. Our experiments show that Video Adapter is capable of incorporating thebroad knowledge and preserving the high fidelity of a large pretrained videomodel in a task-specific small video model that is able to generatehigh-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/.

Probabilistic Adaptation of Text-to-Video Models