This is the code for ECCV 2018 paper MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamics by Xinchen Yan, Akash Rastogi, Ruben Villegas, Kalyan Sunkavalli, Eli Shechtman, Sunil Hadap, Ersin Yumer, Honglak Lee.

Please follow the instructions to run the code.

MT-VAE requires or works with

• Mac OS X or Linux
• NVIDIA GPU

• Install TensorFlow
• Note: this implementation has been tested with TensorFlow 1.3.

• For Human3.6M dataset, please download the pre-processed dataset.

bash prep_human36m_joints.sh

• Disclaimer: Please check the license of Human3.6M dataset if you download this preprocessed version.

• If you want to train the MT-VAE human motion generator, please run the following script (usually it takes 1 day with a single Titan GPU).

bash demo_human36m_trainMTVAE.sh

• Alternatively, you can download the pre-trained MT-VAE model, please run the following script.

bash prep_human36m_model.sh

• Please run the following command to generate multiple diverse human motion given initial motion.

bash demo_human36m_inferMTVAE.sh

• Please run the following command to execute motion analogy-making.

bash demo_human36m_analogyMTVAE.sh

• Please download full Human3.6M videos into the workspace/Human3.6M/ folder.
• We use a pre-trained model from the ICML 2017 HierchVid Repository. Please run the following command for image synthesis given generated motion sequence.

CUDA_VISIBLE_DEVICE=0 python h36m_hierach_gensample.py

• Disclaimer: Please double check the license in that repository and cite HierchVid paper when use.

If you find this useful, please cite our work as follows:

@inproceedings{yan2018mt,
  title={MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamics},
  author={Yan, Xinchen and Rastogi, Akash and Villegas, Ruben and Sunkavalli, Kalyan and Shechtman, Eli and Hadap, Sunil and Yumer, Ersin and Lee, Honglak},
  booktitle={European Conference on Computer Vision},
  pages={276--293},
  year={2018},
  organization={Springer}
}

We would like to thank the amazing developers and the open-sourcing community. Our implementation has especially been benefited from the following excellent repositories:

• Attribute2Image: https://github.com/xcyan/eccv16_attr2img
• TensorFlow-PTN: https://github.com/tensorflow/models/tree/master/research/ptn
• VideoGAN: https://github.com/cvondrick/videogan
• MoCoGAN: https://github.com/sergeytulyakov/mocogan
• HierchVid: https://github.com/rubenvillegas/icml2017hierchvid
• Sketch-RNN: https://github.com/tensorflow/magenta/tree/master/magenta/models/sketch_rnn
• VRNN: https://github.com/jych/nips2015_vrnn
• SVG: https://github.com/edenton/svg