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README.md

Learning to Generate Long-term Future via Hierarchical Prediction

This is the code for the ICML 2017 paper Learning to Generate Long-term Future via Hierarchical Prediction by Ruben Villegas, Jimei Yang, Yuliang Zou, Sungryull Sohn, Xunyu Lin, Honglak Lee.

Please follow the instructions to run the code.

Requirements

This code works with

  • Linux
  • NVIDIA Titan X GPU
  • Tensorflow version 1.3.0

Installing Dependencies (Anaconda installation is recommended)

FFMPEG needs to be installed as well to generate gif videos. If using anaconda, ffmpeg can be installed as follows:

  • conda install -c menpo ffmpeg=3.1.3

Data download and preprocessing

Penn Action:

  • Download data:
    Download from Penn Action and extract into ./datasets/PennAction/
  • Download pose estimated using hourglass network:
./datasets/PennAction/download_hourglass.sh
  • Preprocess:
python ./datasets/PennAction/preprocess.py

Human 3.6M:

  • Download and convert:
    Download from Human 3.6M into ./datasets/Human3.6M/ and preprocess by first converting the pose CDF files into .mat using MATLAB and running the matlab script in ./datasets/Human3.6M/:
cdf2mat.m
  • Download pose estimated using hourglass network:
./datasets/Human3.6M/download_hourglass.sh
  • Preprocess:
python ./datasets/Human3.6M/preprocess.py

Download pre-trained perceptual models for feature loss

./perceptual_models/download.sh

Penn Action training/testing

Training LSTM (can run in parallel with image generator training):

CUDA_VISIBLE_DEVICES=GPU_ID python lstm_src/train_det_rnn_penn.py --gpu=GPU_ID

Training Image Generator (can run in parallel with LSTM training):

CUDA_VISIBLE_DEVICES=GPU_ID python imggen_src/train_penn.py --gpu=GPU_ID

Predict future pose from models trained with the above commands:

CUDA_VISIBLE_DEVICES=GPU_ID python lstm_src/test_det_rnn_penn.py --gpu=GPU_ID --prefix=PENNACTION_DET_LSTM_num_class=8_learning_rate=0.001_image_size=128_batch_size=256_lm_size=13_fut_step=32_num_layer=1_lstm_units=1024_seen_step=10_input_size=26_keep_prob=1.0 --steps=64

Predict video from networks trained with the above commands:

CUDA_VISIBLE_DEVICES=GPU_ID python imggen_src/test_penn.py --gpu=GPU_ID --imggen_prefix=PENNACTION_ANALOGY_imgsize=128_layer=3_alpha=1.0_beta=1.0_gamma=1.0_lr=0.0001 --lstm_prefix=PENNACTION_DET_LSTM_num_class=8_learning_rate=0.001_image_size=128_batch_size=256_lm_size=13_fut_step=32_num_layer=1_lstm_units=1024_seen_step=10_input_size=26_keep_prob=1.0

Resulting images and videos will be located at:

./results/images/PENNACTION_ANALOGY_imgsize=128_layer=3_alpha=1.0_beta=1.0_gamma=1.0_lr=0.0001/

Human 3.6M training/testing

Training LSTM (can run in parallel with image generator training):

CUDA_VISIBLE_DEVICES=GPU_ID python lstm_src/train_det_rnn_h36m.py --gpu=GPU_ID

Training Image Generator (can run in parallel with LSTM training):

CUDA_VISIBLE_DEVICES=GPU_ID python imggen_src/train_h36m.py --gpu=GPU_ID

Predict future pose from models trained with the above commands:

CUDA_VISIBLE_DEVICES=GPU_ID python lstm_src/test_det_rnn_h36m.py --gpu=GPU_ID --prefix=HUMAN3.6M_DET_LSTM_fskip=4_keep_prob=1.0_image_size=128_batch_size=256_lm_size=32_fut_step=32_num_layer=1_lstm_units=1024_seen_step=10_input_size=64_learning_rate=0.001 --steps=128

Predict video from networks trained with the above commands:

CUDA_VISIBLE_DEVICES=GPU_ID python imggen_src/test_h36m.py --gpu=GPU_ID --imggen_prefix=HUMAN3.6M_ANALOGY_imgsize=128_layer=3_alpha=1.0_beta=1.0_gamma=1.0_lr=0.0001 --lstm_prefix=HUMAN3.6M_DET_LSTM_fskip=4_keep_prob=1.0_image_size=128_batch_size=256_lm_size=32_fut_step=32_num_layer=1_lstm_units=1024_seen_step=10_input_size=64_learning_rate=0.001

Resulting images and videos will be located at:

./results/images/HUMAN3.6M_ANALOGY_imgsize=128_layer=3_alpha=1.0_beta=1.0_gamma=1.0_lr=0.0001/

Citation

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

@inproceedings{villegas17hierchvid,
  title={{Learning to Generate Long-term Future via Hierarchical Prediction}},
  author={Villegas, Ruben and Yang, Jimei and Zou, Yuliang and Sohn, Sungryull and Lin, Xunyu and Lee, Honglak},
  booktitle=ICML,
  year={2017}
}

Please contact "ruben.e.villegas@gmail.com" if you have any questions.

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Tensorflow implementation of the ICML 2017 paper: Learning to Generate Long-term Future via Hierarchical Prediction

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