Layered Neural Atlases for Consistent Video Editing

Project Page | Paper

This repository contains an implementation for the SIGGRAPH Asia 2021 paper Layered Neural Atlases for Consistent Video Editing.

The paper introduces the first approach for neural video unwrapping using an end-to-end optimized interpretable and semantic atlas-based representation, which facilitates easy and intuitive editing in the atlas domain.

Installation Requirements

The code is compatible with Python 3.7 and PyTorch 1.6.

You can create an anaconda environment called neural_atlases with the required dependencies by running:

conda create --name neural_atlases python=3.7 
conda activate neural_atlases 
conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 matplotlib tensorboard scipy  scikit-image tqdm  opencv -c pytorch
pip install imageio-ffmpeg gdown
python -m pip install detectron2 -f   https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.6/index.html

Data convention

The code expects 3 folders for each video input, e.g. for a video of 50 frames named “blackswan”:

  1. data/blackswan: A folder of video frames containing image files in the following convention: blackswan/00000.jpg,blackswan/00001.jpg,…,blackswan/00049.jpg (as in the DAVIS dataset).
  2. data/blackswan_flow: A folder with forward and backward optical flow files in the following convention: blackswan_flow/00000.jpg_00001.jpg.npy,blackswan_flow/00001.jpg_00000.jpg,…,blackswan_flow/00049.jpg_00048.jpg.npy.
  3. data/blackswan_maskrcnn: A folder with rough masks (created by Mask-RCNN or any other way) containing files in the following convention: blackswan_maskrcnn/00000.jpg,blackswan_maskrcnn/00001.jpg,…,blackswan_maskrcnn/00049.jpg

For a few examples of DAVIS sequences run:

gdown https://drive.google.com/uc?id=1WipZR9LaANTNJh764ukznXXAANJ5TChe
unzip data.zip

Masks extraction

Given only the video frames folder data/blackswan it is possible to extract the Mask-RCNN masks (and create the required folder data/blackswan_maskrcnn) by running:

python preprocess_mask_rcnn.py --vid-path data/blackswan --class_name bird

where –class_name determines the COCO class name of the sought foreground object. It is also possible to choose the first instance retrieved by Mask-RCNN by using --class_name anything. This is usefull for cases where Mask-RCNN gets correct masks with wrong classes as in the “libby” video:

python preprocess_mask_rcnn.py --vid-path data/libby --class_name anything

Optical flows extraction

Furthermore, the optical flow folder can be extracted using RAFT.
For linking RAFT into the current project run:

git submodule update --init
cd thirdparty/RAFT/
./download_models.sh
cd ../..

For extracting the optical flows (and creating the required folder data/blackswan_flow) run:

python preprocess_optical_flow.py --vid-path data/blackswan --max_long_edge 768

Pretrained models

For downloading a sample set of our pretrained models together with sample edits run:

gdown https://drive.google.com/uc?id=10voSCdMGM5HTIYfT0bPW029W9y6Xij4D
unzip pretrained_models.zip

Training

For training a model on a video, run:

python train.py config/config.json

where the video frames folder is determined by the config parameter "data_folder".
Note that in order to reduce the training time it is possible to reduce the evaluation frequency controlled by the parameter "evaluate_every" (e.g. by changing it to 10000).
The other configurable parameters are documented inside the file train.py.

Evaluation

During training, the model is evaluated. For running only evaluation on a trained folder run:

python only_evaluate.py --trained_model_folder=pretrained_models/checkpoints/blackswan --video_name=blackswan --data_folder=data --output_folder=evaluation_outputs

where trained_model_folder is the path to a folder that contains the config.json and checkpoint files of the trained model.

Editing

To apply editing, run the script only_edit.py. Examples for the supplied pretrained models for “blackswan” and “boat”:

python only_edit.py --trained_model_folder=pretrained_models/checkpoints/blackswan --video_name=blackswan --data_folder=data --output_folder=editing_outputs --edit_foreground_path=pretrained_models/edit_inputs/blackswan/edit_blackswan_foreground.png --edit_background_path=pretrained_models/edit_inputs/blackswan/edit_blackswan_background.png


python only_edit.py --trained_model_folder=pretrained_models/checkpoints/boat --video_name=boat --data_folder=data --output_folder=editing_outputs --edit_foreground_path=pretrained_models/edit_inputs/boat/edit_boat_foreground.png --edit_background_path=pretrained_models/edit_inputs/boat/edit_boat_backgound.png

Where edit_foreground_path and edit_background_path specify the paths to 1000×1000 images of the RGBA atlas edits.

For applying an edit that was done on a frame (e.g. for the pretrained “libby”):

python only_edit.py --trained_model_folder=pretrained_models/checkpoints/libby --video_name=libby --data_folder=data --output_folder=editing_outputs  --use_edit_frame --edit_frame_index=7 --edit_frame_path=pretrained_models/edit_inputs/libby/edit_frame_.png

Citation

If you find our work useful in your research, please consider citing:

@article{kasten2021layered,
  title={Layered Neural Atlases for Consistent Video Editing},
  author={Kasten, Yoni and Ofri, Dolev and Wang, Oliver and Dekel, Tali},
  journal={arXiv preprint arXiv:2109.11418},
  year={2021}
}

GitHub

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