# 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  
**arXiv:** [2401.12945](https://arxiv.org/abs/2401.12945)  
**Date:** 2024-01-23  
**Summary page:** https://www.summarizepaper.com/en/paper/2401.12945v2/

## Key points

- Lumiere is a text-to-video diffusion model designed to synthesize realistic and coherent motion videos.
- The key challenge addressed by Lumiere is the generation of diverse and lifelike video content.
- Authors propose a Space-Time U-Net architecture that can generate the entire temporal duration of a video in one pass through the model.
- Results from Lumiere show state-of-the-art performance in text-to-video generation tasks.
- Lumiere's design allows for various applications such as image-to-video conversion, video inpainting, and stylized video generation.
- The versatility of Lumiere makes it valuable for enhancing visual storytelling capabilities in entertainment production and digital media creation.

## Summary

In their paper titled "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 and Inbar Mosseri introduce Lumiere as a text-to-video diffusion model aimed at synthesizing videos that depict realistic and coherent motion. The key challenge addressed by Lumiere is the generation of diverse and lifelike video content. To achieve this goal,
the authors propose a novel Space-Time U-Net architecture that can generate the entire temporal duration of a video in a single pass through the model. This approach allows the model to learn how to directly generate full-frame-rate low-resolution videos by processing them at multiple space-time scales. The results obtained from Lumiere demonstrate state-of-the-art performance in text-to-video generation tasks. Moreover,
the design of Lumiere enables its application across various content creation tasks and video editing applications such as image-to-video conversion,
video inpainting for filling missing or corrupted parts of videos seamlessly
and stylized video generation. The versatility of Lumiere makes it a valuable tool for enhancing visual storytelling capabilities in fields like entertainment production and digital media creation. For more information about Lumiere and to view examples of its capabilities in action,
interested readers can visit the project webpage at https://lumiere-video.github.io/
or watch a demonstration video at https://www.youtube.com/watch?v=wxLr02Dz2Sc.