An official implementation of the paper "Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction", NeurIPS, 2019. [paper] [supp]
- Linux
- NVIDIA GeForce GTX 1080Ti
- Tensorflow 1.12.0
- Python3 (>= 3.5.2)
You can install packages by running pip install -r requirements.txt.
Or you can download our prebuilt docker image,
by running docker pull join16/python3-cuda:3.5-cuda9.0-nips2019
.
If you want, you can build docker image manually,
by running docker build -t {image_name} .
This code is for the Penn Action dataset. The dataset can be downloaded here. After download PennAction.tar.gz, unzip and then run following code to prepare dataset.
./prepare_penn_dataset.sh {unzipped_original_dataset_dir}
For the training, pretrained VGG19 network is needed. It can be downloaded here.
python train.py --mode detector_translator --config configs/penn.yaml
python make_pseudo_labels.py --config configs/penn.yaml --checkpoint {path/to/detector_translator/checkpoint}
python train.py --mode motion_generator --config configs/penn.yaml
python evaluate.py --config configs/penn.yaml \
--checkpoint_stage1 {path/to/detector_translator/checkpoint} \
--checkpoint_stage2 {path/to/motion_generator/checkpoint} \
--save_dir {path/to/save/results}
Learning to Generate Long-term Future via Hierarchical Prediction, Villegas et. al., ICML, 2017. [code]
Hierarchical Long-term Video Prediction without Supervision, Wichers et. al., ICML, 2018. [code]
Flow-Grounded Spatial-Temporal Video Prediction from Still Images, Li et. al., ECCV, 2018. [code]
Please cite our paper when you use this code.
@inproceedings{yunji_neurips_2019,
title={Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction},
author={Kim, Yunji and Nam, Seonghyeon and Cho, In and Kim, Seon Joo},
booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
year={2019}
}