MAKE-A-VIDEO: TEXT-TO-VIDEO GENERATION WITHOUT TEXT-VIDEO DATA

Abstract

We propose Make-A-Video -an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V). Our intuition is simple: learn what the world looks like and how it is described from paired text-image data, and learn how the world moves from unsupervised video footage. Make-A-Video has three advantages: (1) it accelerates training of the T2V model (it does not need to learn visual and multimodal representations from scratch), (2) it does not require paired text-video data, and (3) the generated videos inherit the vastness (diversity in aesthetic, fantastical depictions, etc.) of today's image generation models. We design a simple yet effective way to build on T2I models with novel and effective spatial-temporal modules. First, we decompose the full temporal U-Net and attention tensors and approximate them in space and time. Second, we design a spatial temporal pipeline to generate high resolution and frame rate videos with a video decoder, interpolation model and two super resolution models that can enable various applications besides T2V. In all aspects, spatial and temporal resolution, faithfulness to text, and quality, Make-A-Video sets the new state-of-the-art in text-to-video generation, as determined by both qualitative and quantitative measures.

1. INTRODUCTION

The Internet has fueled collecting billions of (alt-text, image) pairs from HTML pages (Schuhmann et al., 2022) , enabling the recent breakthroughs in Text-to-Image (T2I) modeling. However, replicating this success for videos is limited since a similarly sized (text, video) dataset cannot be easily collected. It would be wasteful to train Text-to-Video (T2V) models from scratch when there already exist models that can generate images. Moreover, unsupervised learning enables networks to learn from orders of magnitude more data. This large quantity of data is important to learn representations of more subtle, less common concepts in the world. Unsupervised learning has long had great success in advancing the field of natural language processing (NLP) (Liu et al., 2019a; Brown et al., 2020) . Models pre-trained this way yield considerably higher performance than when solely trained in a supervised manner. Inspired by these motivations, we propose Make-A-Video. Make-A-Video leverages T2I models to learn the correspondence between text and the visual world, and uses unsupervised learning on unlabeled (unpaired) video data, to learn realistic motion. Together, Make-A-Video generates videos from text without leveraging paired text-video data. Clearly, text describing images does not capture the entirety of phenomena observed in videos. That said, one can often infer actions and events from static images (e.g. a woman drinking coffee, or an



+ Core Contributors. Corresponding author: urielsinger@meta.com. Jie and Songyang are from University of Rochester (work done during internship at Meta).

