Upper body image synthesis from skeleton(Keypoints). Pose2Img module in the ICCV-2021 paper "Speech Drives Templates: Co-Speech Gesture Synthesis with Learned Templates". [arxiv / github]

This is a modified implementation of Synthesizing Images of Humans in Unseen Poses.

To install dependencies, run

pip install -r requirements.txt

To run this module, you need two NVIDIA gpus with at least 11 GB respectively. Our code is tested on Ubuntu 18.04LTS with Python3.6.

• We provide the dataset and pretrained models of Oliver at here.
• According to $ROOT/configs/yaml/Oliver.yaml:

1. Unzip and put the data to $ROOT/data/Oliver
2. Put the pretrained model to $ROOT/ckpt/Oliver/ckpt_final.pth

1. Train Script:

python main.py \
    --name Oliver \
    --config_path configs/yaml/Oliver.yaml \
    --batch_size 1 \

2. Run Tensorboard for training visualization.

tensorboard --logdir ./log --port={$Port} --bind_all

Generate a realistic video for Oliver from {keypoints}.npz.

python inference.py \
   --cfg_path cfg/yaml/Oliver.yaml \
   --name demo \
   --npz_path target_pose/Oliver/varying_tmplt.npz \
   --wav_path target_pose/Oliver/varying_tmplt.mp4

• In the result directory, you can find jpg files which correspond to the npz.

• For your own dataset, you need to modify custom config.yaml.

• Prepare the keypoints using OpenPose.

• The raw keypoints for each frame is of shape (3, 137) which is composed of [pose(3,25), face(3,70), left_hand(3,21),right_hand(3,21)]

  The definition is as follows:

  

If you find this code useful for your research, please use the following BibTeX entry.

@inproceedings{qian2021speech,
  title={Speech Drives Templates: Co-Speech Gesture Synthesis with Learned Templates},
  author={Qian, Shenhan and Tu, Zhi and Zhi, YiHao and Liu, Wen and Gao, Shenghua},
  journal={International Conference on Computer Vision (ICCV)},
  year={2021}
}