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arXiv · 2311.10709

Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning

Rohit Girdhar·Mannat Singh·Andrew Brown·Quentin Duval·Samaneh Azadi·Sai Saketh Rambhatla·Akbar Shah·Xi Yin·Devi Parikh·Ishan Misra
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Abstract

We present Emu Video, a text-to-video generation model that factorizes the generation into two steps: first generating an image conditioned on the text, and then generating a video conditioned on the text and the generated image. We identify critical design decisions--adjusted noise schedules for diffusion, and multi-stage training that enable us to directly generate high quality and high resolution videos, without requiring a deep cascade of models as in prior work. In human evaluations, our generated videos are strongly preferred in quality compared to all prior work--81% vs. Google's Imagen Video, 90% vs. Nvidia's PYOCO, and 96% vs. Meta's Make-A-Video. Our model outperforms commercial solutions such as RunwayML's Gen2 and Pika Labs. Finally, our factorizing approach naturally lends itself to animating images based on a user's text prompt, where our generations are preferred 96% over prior work.

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Rohit Girdhar, Mannat Singh, Andrew Brown, Quentin Duval, Samaneh Azadi, Sai Saketh Rambhatla, Akbar Shah, Xi Yin, Devi Parikh, Ishan Misra. 2023-11-17. Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning. https://arxiv.org/abs/2311.10709

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