COMPUTER VISION
CORE MACHINE LEARNING
Factorizing Text-to-Video Generation by Explicit Image Conditioning
July 29, 2024
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.
AUTHORS
Written by
Andrew Brown
Quentin Duval
Samaneh Azadi
Saketh Rambhatla
Mian Akbar Shah
Xi Yin
Devi Parikh
Publisher
ECCV
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