Paper

The right most square is the final result. Softmax mask (circles):


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Heatmap mask:



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conda env create -f environment.yml
conda activate venv11
We use pytorch 1.7.1 with python 3.8.
Please obtain pretrained keypoint module. You can do so by
git checkout fomm-new-torch
Then, follow the instructions from the README of that branch, or obtain a pre-trained checkpoint from
https://github.com/AliaksandrSiarohin/first-order-model

to train a model on specific dataset run:

CUDA_VISIBLE_DEVICES=0,1,2,3 python run.py --config config/dataset_name.yaml --device_ids 0,1,2,3 --checkpoint_with_kp path/to/checkpoint/with/pretrained/kp

E.g. taichi-256-q.yaml for the keypoint heatmap mask model, and taichi-256-softmax-q.yaml for drawn circular keypoints instead.

the code will create a folder in the log directory (each run will create a time-stamped new directory). checkpoints will be saved to this folder. to check the loss values during training see log.txt. you can also check training data reconstructions in the train-vis sub-folder. by default the batch size is tuned to run on 4 titan-x gpu (apart from speed it does not make much difference). You can change the batch size in the train_params in corresponding .yaml file.

To evaluate the reconstruction of the driving video from its first frame, run:

CUDA_VISIBLE_DEVICES=0 python run.py --config config/dataset_name.yaml --mode reconstruction --checkpoint path/to/checkpoint --checkpoint_with_kp path/to/checkpoint/with/pretrained/kp

you will need to specify the path to the checkpoint, the reconstruction sub-folder will be created in the checkpoint folder. the generated video will be stored to this folder, also generated videos will be stored in png subfolder in loss-less '.png' format for evaluation. instructions for computing metrics from the paper can be found: https://github.com/aliaksandrsiarohin/pose-evaluation.

In order to animate a source image with motion from driving, run:

CUDA_VISIBLE_DEVICES=0 python run.py --config config/dataset_name.yaml --mode animate --checkpoint path/to/checkpoint --checkpoint_with_kp path/to/checkpoint/with/pretrained/kp

you will need to specify the path to the checkpoint, the animation sub-folder will be created in the same folder as the checkpoint. you can find the generated video there and its loss-less version in the png sub-folder. by default video from test set will be randomly paired, but you can specify the "source,driving" pairs in the corresponding .csv files. the path to this file should be specified in corresponding .yaml file in pairs_list setting.

1. taichi. follow the instructions in data/taichi-loading or instructions from https://github.com/aliaksandrsiarohin/video-preprocessing.

1. resize all the videos to the same size e.g 256x256, the videos can be in '.gif', '.mp4' or folder with images. we recommend the later, for each video make a separate folder with all the frames in '.png' format. this format is loss-less, and it has better i/o performance.

2. create a folder data/dataset_name with 2 sub-folders train and test, put training videos in the train and testing in the test.

3. create a config config/dataset_name.yaml, in dataset_params specify the root dir the root_dir: data/dataset_name. also adjust the number of epoch in train_params.

citation:

@misc{toledano2021,
  author = {Or Toledano and Yanir Marmor and Dov Gertz},
  title = {Image Animation with Keypoint Mask},
  year = {2021},
  eprint={2112.10457},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}