Awesome

SHOW: Synchronous HOlistic body in the Wild

[CVPR2023] Generating Holistic 3D Human Motion from Speech

This repository provides the official implementation of SHOW(Synchronous HOlistic body in the Wild). Given rgb images or videos only, SHOW can reconstruct holistic whole body mesh results. Please refer to the arXiv paper for more details.

What you can use:

easy and efficient: adapts SMPLify-X to the videos of talking persons with several good practices.
state-of-art: Compared to other methods, ours produces more accurate and stable results with details.

Getting Started

Take a quick tour on colab: [Colab].

another [colab] version from @SlimeVRX

Installation

To install SHOW, please execute pip install git+https://github.com/yhw-yhw/SHOW.git or

git clone https://github.com/yhw-yhw/SHOW.git
cd SHOW && pip install -v -e .

Preliminary

[environment] Using virtual environment by runing

conda create -n env_SHOW python=3.9
eval "$(conda shell.bash hook)"
conda activate env_SHOW

install pytorch using pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116 or

conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia

You can run

cd SHOW
cd modules/MICA && pip install -r requirements.txt
cd ../PIXIE && pip install -r requirements.txt
cd ../PyMAF && pip install -r requirements.txt
cd ../DECA && pip install -r requirements.txt
cd ../.. && pip install -r requirements.txt

Note that Pytorch3D may require manuall installation (see instructions here).

[models] download packed model files, and put it in the same level directory as SHOW

wget "https://www.dropbox.com/scl/fi/gwvp5c3yijkjc726bidxx/models.zip?rlkey=2p4m788qpi04oye3kur2pxszx&st=dchhjclv&dl=0" -O models.zip
wget "https://www.dropbox.com/scl/fi/vcav90wzwqxmg56n42gr1/data.zip?rlkey=5oetna909azec027v42ogx42q&st=e5mnsldy&dl=0" -O data.zip
unzip data.zip
7za x models.zip

[OpenPose]: follow the code from [OpenPose Colab notebook](https://colab.research.google.com/github/tugstugi/dl-colab-notebooks/blob/master/notebooks/OpenPose.ipynb, and change OpenPose bin path openpose_root_path and openpose_bin_path in configs\configs\machine_cfg.py.

[MMPose]: Make sure to install mmcv-full, and set env variable mmpose_root

pip install openmim
mim install mmcv-full
git clone https://github.com/open-mmlab/mmdetection
cd /content/mmdetection && python setup.py install
git clone https://github.com/open-mmlab/mmpose
export mmpose_root = $mmpose_root$

models for inisghtface:

use the following command as reference

mkdir -p ~/.insightface/models
cd ~/.insightface/models
wget https://keeper.mpdl.mpg.de/f/2d58b7fed5a74cb5be83/?dl=1 -O antelopev2.zip
wget https://keeper.mpdl.mpg.de/f/8faabd353cfc457fa5c5/?dl=1 -O buffalo_l.zip
mkdir -p antelopev2 && cd antelopev2 && unzip -o ../antelopev2.zip
mkdir -p buffalo_l && cd buffalo_l && unzip -o ../buffalo_l.zip

[others] pip uninstall -y xtcocotools && pip install xtcocotools --no-binary xtcocotools

Run

Run SHOW on demo video

python main.py --speaker_name -1 --all_top_dir ./test/demo_video/half.mp4

It takes 15-20 minutes for 5s 30 FPS video on Colab Tesla T4.

The final results are stored in ./test/demo_video/ours_exp. All the smplx variables can be found in ./test/demo_video/ours_exp/final_all.pkl, and the visualization can be viewed in ./test/demo_video/ours_exp/final_vis.mp4.

Datasets

Download Dataset Videos

Download all videos from youtube, please refer to (https://github.com/amirbar/speech2gesture), or using the following script: download_youtube.py, remember to install yt-dlp. After downloading all videos, you can using SHOW_intervals_subject4.csv for video interval cropping. oOr download it from https://www.dropbox.com/sh/k3k5iq0hg0y1mjm/AAAar3ObkFPkP4c87sDMggrta?dl=0

Visualize Dataset

After running SHOW, we will get processed data in a pkl file. Here we can visualize pkl from our provided dataset.

python render_pkl_release.py \
--pkl_file_path test/demo_pkl/all.pkl \
--out_images_path test/demo_pkl/ours_images \
--output_video_path test/demo_pkl/ours.mp4 \
--smplx_model_path ../models/smplx/SMPLX_NEUTRAL_2020_org.npz

Download Links

The data reconstructed by SHOW is released, you can download it

[Dropbox]

Dataset Description

speaker=oliver/chemistry/conan/seth
The maximum length of video clip is 10s with 30 fps

Format of files in the compressed package:

{speaker}_wav_tar.tar.gz:
- The path format of each file is: speaker/video_fn/seq_fn.wav
- Audio obtained from the original video at 22k sampling rate

{speaker}_pkl_tar.tar.gz:

The path format of each file is: speaker/video_fn/seq_fn.pkl
Data contained in the pkl file:

width，height: the video width and height
center: the center point of the video
batch_size: the sequence length
camera_transl: the displacement of the camera
focal_length: the pixel focal length of a camera
body_pose_axis: (bs, 21, 3)
expression: (bs, 100)
jaw_pose: (bs,3)
betas: (300)
global_orient: (bs,3)
transl: (bs,3)
left_hand_pose: (bs,12)
right_hand_pose: (bs,12)
leye_pose: (bs,3)
reye_pose: (bs,3)
pose_embedding: (bs,32)

Set the config of smplx model as follows:

  smplx_cfg=dict(
      model_path='path_to_smplx_model'
      model_type= 'smplx',
      gender= 'neutral',
      use_face_contour= True,
      use_pca= True,
      flat_hand_mean= False,
      use_hands= True,
      use_face= True,
      num_pca_comps= 12,
      num_betas= 300,
      num_expression_coeffs= 100,
  )

In practice, global orient and transl parameters should be fixed as the first frame and the lower part of the body pose should be fixed as sitting or standing position: code

SMPLX expression dim convert tool

code

usage

python cvt_exp_dim_tool.py \
--target-exp-dim 50 \
--pkl-path ./rich.npy \
--model-path ../models/smplx/SMPLX_MALE_shape2019_exp2020.npz

Citation

If you use this project in your research please cite this paper:

@inproceedings{yi2022generating,
  title={Generating Holistic 3D Human Motion from Speech},
  author={Yi, Hongwei and Liang, Hualin and Liu, Yifei and Cao, Qiong and Wen, Yandong and Bolkart, Timo and Tao, Dacheng and Black, Michael J},
  booktitle={CVPR},
  year={2023}
}

Issues

If following error is encountered

  RuntimeError: Subtraction, the `-` operator, with a bool tensor is not supported. If you are trying to invert a mask, use the `~` or `logical_not()` operator instead.

Open torchgeometry/core/conversions.py file and replace line 304-306 with

 mask_c1 = mask_d2 * (~ mask_d0_d1)
 mask_c2 = (~ mask_d2) * mask_d0_nd1
 mask_c3 = (~ mask_d2) * (~ mask_d0_nd1)

License

This code and model are available for non-commercial scientific research purposes as defined in the LICENSE file. By downloading and using the code and model you agree to the terms in the LICENSE.

Acknowledgements

For functions or scripts that are based on external sources, we acknowledge the origin individually in each file. We specifically thanks WOJCIECH ZIELONKA and Justus Thies for sharing their face tracking codebase.

Here are some great resources we benefit:

SMPLify-X
DECA for face expression initialization
PIXIE and PyMAF-X for SMPL-X parameters initialization
DeepLab for person segmentation
MICA and [https://github.com/HavenFeng/photometric_optimization] for face tracking
MICA_Tracker
Pytorch3D for rendering
FAN for landmark detection
arcface-pytorch

Contact

For questions, please contact talkshow@tue.mpg.de or hongwei.yi@tuebingen.mpg.de or fthualinliang@mail.scut.edu.cn.

For commercial licensing, please contact ps-licensing@tue.mpg.de