Text2LIVE: Text-Driven Layered Image and Video Editing

 

Abstract

We present a method for zero-shot, text-driven appearance manipulation in natural images and videos. Specifically, given an input image or video and a target text prompt, our goal is to edit the appearance of existing objects (e.g., object's texture) or augment the scene with new visual effects (e.g., smoke, fire) in a semantically meaningful manner. Our framework trains a generator using an internal dataset of training examples, extracted from a single input (image or video and target text prompt), while leveraging an external pre-trained CLIP model to establish our losses. Rather than directly generating the edited output, our key idea is to generate an edit layer (color+opacity) that is composited over the original input. This allows us to constrain the generation process and maintain high fidelity to the original input via novel text-driven losses that are applied directly to the edit layer. Our method neither relies on a pre-trained generator nor requires user-provided edit masks. Thus, it can perform localized, semantic edits on high-resolution natural images and videos across a variety of objects and scenes.  

Semi-Transparent Effects

Text2LIVE successfully augments the input scene with complex semi-transparent effects without changing irrelevant content in the image.

Paper

Text2LIVE: Text-Driven Layered Image and Video Editing Omer Bar-Tal*, Dolev Ofri-Amar*, Rafail Fridman*, Yoni Kasten, Tali Dekel. (* indicates equal contribution) ArXiv [paper]

Supplementary Material

[supplementary page]

Bibtex

  @article{Text2LIVE2022,
	  author    = {Omer Bar-Tal and Dolev Ofri-Amar and Rafail Fridman and Yoni Katen and Tali Dekel},
  	  title     = {Text2LIVE: Text-Driven Layered Image and Video Editing},
	  journal   = {arXiv preprint arXiv:2204.02491},
	  year      = {2022},
  }
			  

Acknowledgments

We thank Kfir Aberman, Lior Yariv, Shai Bagon, and Narek Tumanayan for their insightful comments. We thank Narek Tumanayan for his help with the baselines comparison.