Text2LIVE is a method for text-driven editing of real-world images and videos, as described in (link to paper).

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.

git clone https://github.com/omerbt/Text2LIVE.git
conda create --name text2live python=3.9 
conda activate text2live 
pip install -r requirements.txt

Download sample images and videos from the DAVIS dataset:

cd Text2LIVE
gdown https://drive.google.com/uc?id=1osN4PlPkY9uk6pFqJZo8lhJUjTIpa80J&export=download
unzip data.zip

It will create a folder data:

Text2LIVE
├── ...
├── data
│   ├── pretrained_nla_models # NLA models are stored here
│   ├── images # sample images
│   └── videos # sample videos from DAVIS dataset
│         ├── car-turn # contains video frames 
│         ├── ...
└── ...

To enforce temporal consistency in video edits, we utilize the Neural Layered Atlases (NLA). Pretrained NLA models are taken from here, and are already inside the data folder.

• Our method is designed to change textures of existing objects / augment the scene with semi-transparent effects (e.g., smoke, fire). It is not designed for adding new objects or significantly deviating from the original spatial layout.
• Training Text2LIVE multiple times with the same inputs can lead to slightly different results.
• CLIP sometimes exhibits bias towards specific solutions (see figure 9 in the paper), thus slightly different text prompts may lead to different flavors of edits.

The required GPU memory depends on the input image/video size, but you should be good with a Tesla V100 32GB :). Currently mixed precision introduces some instability in the training process, but it could be added later.

Run the following command to start training

python train_video.py --example_config car-turn_winter.yaml

Run the following command to start training

python train_image.py --example_config golden_horse.yaml

Intermediate results will be saved to results during optimization. The frequency of saving intermediate results is indicated in the log_images_freq flag of the configuration.

For more see the supplementary material.

@inproceedings{bar2022text2live,
  title={Text2live: Text-driven layered image and video editing},
  author={Bar-Tal, Omer and Ofri-Amar, Dolev and Fridman, Rafail and Kasten, Yoni and Dekel, Tali},
  booktitle={European Conference on Computer Vision},
  pages={707--723},
  year={2022},
  organization={Springer}
}