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Probabilistic Adaptation of Text-to-Video Models by Yang, Mengjiao; Du, Yilun; Dai, Bo; Schuurmans, Dale; Tenenbaum, Joshua B.; Abbeel, Pieter is a scholarly article available to read on EtoBox.

What is Probabilistic Adaptation of Text-to-Video Models about?

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

Author

Yang, Mengjiao; Du, Yilun; Dai, Bo; Schuurmans, Dale; Tenenbaum, Joshua B.; Abbeel, Pieter

Published

2023

Language

EN