Lumiere: A Space-Time Diffusion Model for Video Generation

• URL: http://arxiv.org/abs/2401.12945v2
• Date: Mon, 5 Feb 2024 16:36:30 GMT
• Title: Lumiere: A Space-Time Diffusion Model for Video Generation
• Authors: Omer Bar-Tal, Hila Chefer, Omer Tov, Charles Herrmann, Roni Paiss, Shiran Zada, Ariel Ephrat, Junhwa Hur, Guanghui Liu, Amit Raj, Yuanzhen Li, Michael Rubinstein, Tomer Michaeli, Oliver Wang, Deqing Sun, Tali Dekel, Inbar Mosseri
• Abstract summary: We introduce a Space-Time U-Net architecture that generates the entire temporal duration of the video at once. This is in contrast to existing video models which synthesize distants followed by temporal super-resolution. By deploying both spatial and (importantly) temporal down- and up-sampling, our model learns to directly generate a full-frame-rate, low-resolution video.
• Score: 75.54967294846686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthesis. To this end, we introduce a Space-Time U-Net architecture that generates the entire temporal duration of the video at once, through a single pass in the model. This is in contrast to existing video models which synthesize distant keyframes followed by temporal super-resolution -- an approach that inherently makes global temporal consistency difficult to achieve. By deploying both spatial and (importantly) temporal down- and up-sampling and leveraging a pre-trained text-to-image diffusion model, our model learns to directly generate a full-frame-rate, low-resolution video by processing it in multiple space-time scales. We demonstrate state-of-the-art text-to-video generation results, and show that our design easily facilitates a wide range of content creation tasks and video editing applications, including image-to-video, video inpainting, and stylized generation.

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