DIGAN (ICLR 2022)

Official PyTorch implementation of "Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks" by Sihyun Yu*^,1, Jihoon Tack*^,1, Sangwoo Mo*^,1, Hyunsu Kim², Junho Kim², Jung-Woo Ha², Jinwoo Shin¹.
¹KAIST, ²NAVER AI Lab (KAIST-NAVER Hypercreative AI Center)

TL;DR: We make video generation scalable leveraging implicit neural representations.
paper | project page

Illustration of the (a) generator and (b) discriminator of DIGAN. The generator creates a video INR weight from random content and motion vectors, which produces an image that corresponds to the input 2D grids {(x, y)} and time t. Two discriminators determine the reality of each image and motion (from a pair of images and their time difference), respectively.

1. Environment setup

conda create -n digan python=3.8
conda activate digan

pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html

pip install hydra-core==1.0.6
pip install tqdm scipy scikit-learn av ninja
pip install click gitpython requests psutil einops tensorboardX

2. Dataset

One should organize the video dataset as follows:

UCF-101

UCF-101
|-- train
    |-- class1
        |-- video1.avi
        |-- video2.avi
        |-- ...
    |-- class2
        |-- video1.avi
        |-- video2.avi
        |-- ...
    |-- ...

Other video datasets (Sky Time lapse, TaiChi-HD, Kinetics-food)

Video dataset
|-- train
    |-- video1
        |-- frame00000.png
        |-- frame00001.png
        |-- ...
    |-- video2
        |-- frame00000.png
        |-- frame00001.png
        |-- ...
    |-- ...
|-- val
    |-- video1
        |-- frame00000.png
        |-- frame00001.png
        |-- ...
    |-- ...

Dataset download

• Link: UCF-101, Sky Time lapse, TaiChi-HD
• For Kinetics-food dataset, read prepare_data/README.md

3. Training

To train the model, navigate to the project directory and run:

python src/infra/launch.py hydra.run.dir=. +experiment_name=<EXP_NAME> +dataset.name=<DATASET>

You may change training options via modifying configs/main.yml and configs/digan.yml.
Also the dataset list is as follows, <DATASET>: {UCF-101,sky,taichi,kinetics}. The default data path is /data, where you can change it via modifying configs/main.yml.

4. Evaluation (FVD and KVD)

python src/scripts/compute_fvd_kvd.py --network_pkl <MODEL_PATH> --data_path <DATA_PATH>

5. Video generation

Genrate and visualize videos (as gif and mp4):

python src/scripts/generate_videos.py --network_pkl <MODEL_PATH> --outdir <OUTPUT_PATH>

6. Results

Generated video results of DIGAN on TaiChi (top) and Sky (bottom) datasets.
More generated video results are available at the following site.
One can download the pretrained checkpoints from the following link.

Citation

@inproceedings{
    yu2022digan,
    title={Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks},
    author={Yu, Sihyun and Tack, Jihoon and Mo, Sangwoo and Kim, Hyunsu and Kim, Junho and Ha, Jung-Woo and Shin, Jinwoo},
    booktitle={International Conference on Learning Representations},
    year={2022},
}

Reference

This code is mainly built upon StyleGAN2-ada and INR-GAN repositories.
We also used the code from following repositories: DiffAug, VideoGPT, MDGAN

Lisence

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