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.

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AUTHORS

Written by

Rohit Girdhar

Mannat Singh

Andrew Brown

Quentin Duval

Samaneh Azadi

Saketh Rambhatla

Mian Akbar Shah

Xi Yin

Devi Parikh

Ishan Misra

Publisher

ECCV

Research Topics

Computer Vision

Core Machine Learning

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