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Resolving 3D Human Pose Ambiguities with 3D Scene Constraints

[Project Page] [Paper]

PROX Examples

Note

The released version of PROX code depends on the released versions of SMPLify-X and Vposer on github. We just realized that these versions differ from our internal versions and that, consequently, the released version of PROX produces results which differ from the results reported in the paper.

We would like to inform you about this finding. After the CVPR deadline, we will release our reference code that reproduces the paper results.

We apologize for the confusion and will work on resolving this.

License

Software Copyright License for non-commercial scientific research purposes. Please read carefully the terms and conditions and any accompanying documentation before you download and/or use the SMPL-X/SMPLify-X/PROX model, data and software, (the "Model & Software"), including 3D meshes, blend weights, blend shapes, textures, software, scripts, and animations. By downloading and/or using the Model & Software (including downloading, cloning, installing, and any other use of this github repository), you acknowledge that you have read these terms and conditions, understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you must not download and/or use the Model & Software. Any infringement of the terms of this agreement will automatically terminate your rights under this License

Description

This repository contains the fitting code used for the experiments in Resolving 3D Human Pose Ambiguities with 3D Scene Constraints.

PROX Dataset

To run the fitting code, you would need to downlaod and extract at least one of the PROX datasets. The webpage provides the 2 PROX datasets:

  • Quantitative PROX dataset: Dataset of 180 static RGB-D frames with Ground Truth. The dataset captures static RGB-D frames of 1 subject in 1 scene and is described in Section 4.2 of the PROX paper.

  • Qualitative PROX dataset: Dataset of 100K RGB-D frames pseudo Ground Truth. The dataset captures dynamic RGB-D sequences of 20 subjects in 12 scenes and is described in Section 4.1.2 on the PROX paper.

Both datasets have a very similar structure which is explained next. After extracting the dataset, you should have a directory with the following structure:

prox_qualitative_dataset
├── body_segments
├── calibration
├── cam2world
├── fittings
├── keypoints
├── keypoints_overlay
├── recordings
├── scenes
└── sdf

The content of each folder is explained below:

  • body_segments contains the contact body parts.
  • calibration contains the calibration information of the Color and IR cameras of the Kinect-One sensor.
  • cam2world contains the camera-to-world transformation matrices to spatially align the camera to the 3D scene scans.
  • fittings contains SMPL-X fittings parameters.
  • keypoints contains 2D keypoints in json files computed from openpose.
  • keypoints_overlay contains 2D keypoints overlayed on the RGB images as generatd by openpose.
  • scenes 3D scene meshes.
  • sdf Signed Distance Field of the 3D scenes.

Recordings Documentation

recordings contains the raw RGB-D recordings. The prox dataset come with 60 recordings, each recording folder name has the format of SceneName_SubjectID_SequenceID. Each recording folder includes the following sub_folders:

SceneName_SubjectID_SequenceID
├── BodyIndex
├── BodyIndexColor
├── Color
├── Depth
└── Skeleton

BodyIndex: Human masks computed by Kinect-One SDK (png, 512x424 px). BodyIndexColor: Human masks computed by running DeepLabV3 on the color fames. (png, 1920x1080 px). Color: RGB frames (jpg, 1920x1080 px). Depth: Depth frames (png, 512x424 px, ToF camera). Infrared: Infrared images (png, 512x424 px). Skeleton: Body skeletons captured by Kinect-One SDK (json).

Visualization

You can visualize the raw data by running the script:

python prox/viz/viz_raw_data.py RECORDING_DIR --show_color 1 --show_body_only 1

Color and Depth alignment

The color and depth frame of the kinect are not spatially aligned and they don't have the same resolution. To project one frame to another, you can use the follwing command:

python prox/align_RGBD.py RECORDING_DIR --mode MODE

where mode can be depth2color or color2depth.

Quantitative PROX dataset

The Quantitative PROX dataset has the same structure as explained above in additon to one file vicon2scene.json which contains transformation matrix from Vicon coordintates system to the 3D scene coordintates system. The fitting folder of the quantitative dataset contains SMPL-X fittings computed using [MoSH++] You can visualize MoSH results by running the following command:

python prox/viz/viz_mosh.py FITTING_DIR
--base_dir  ~/prox_dataset/quantitative --model_folder ~/prox_dataset/models/ --gender male

For example:

python prox/viz/viz_mosh.py ~/prox_dataset/quantitative/fittings/mosh/vicon_03301_01/
--base_dir  ~/prox_dataset/quantitative --model_folder ~/prox_dataset/models/ --gender male

Fitting

To run the method you would first need to need to download and extract the PROX dataset as explained in the previous section. Then run the following command to execute the code:

python prox/main.py --config cfg_files/CONF.yaml
    --recording_dir RECORDING_DIR
    --output_folder OUTPUT_FOLDER
    --visualize="True/False"
    --model_folder MODEL_FOLDER
    --vposer_ckpt VPOSER_FOLDER
    --part_segm_fn smplx_parts_segm.pkl

where the RECORDING_DIR is a path to one of the recordings from the PROX dataset. CONF is the fitting configuration, which code be: RGB, PROX, SMPLifyD or PROXD. For example:

python prox/main.py --config cfg_files/PROX.yaml
    --recording_dir ~/prox_dataset/recordings/N3OpenArea_00157_01
    --output_folder ~/PROX_results
    --vposer_ckpt ~/prox_dataset/models/vposer_v1_0/
     --part_segm_fn ~/prox_dataset/models/smplx_parts_segm.pkl
      --model_folder ~/prox_dataset/models

This will generate several results: pkl files which include SMPL-X parameters, SMPL-X body meshes, rendering of the fitting results overlayed on the color images, rendering of the body in the 3D scene.

You can also visualize the results in 3D by running the following script:

prox/viz/viz_fitting.py FITTING_DIR --base_dir BASE_DIR --model_folder ~/prox_dataset/models --gender GENDER

where the FITTING_DIR is a directory that contains the SMPL-X pkl parameters.

PROXD Fittings

We provide PROXD fittings for the dataset on the website as well as preview videos. We provide the fittings as .pkl files which contains the SMPL-X parameters. For more details on SMPL-X parameterization and formulation, check this repository SMPL-X. Similarly; you can visualize the results in 3D by running the following script:

prox/viz/viz_fitting.py FITTING_DIR --base_dir BASE_DIR --model_folder MODEL_FOLDER

You can also create meshes from the .pkl files and render the results using:

prox/renderer.pkl FITTING_DIR --base_dir BASE_DIR --model_folder MODEL_FOLDER

Dependencies

Install requirements:

pip install -r requirements.txt

Then follow the installation instructions for each of the following before using the fitting code.

  1. Mesh Packages
  2. Chamfer Distance
  3. PyTorch Mesh self-intersection for interpenetration penalty

The code has been tested with Python 3.6, CUDA 10.0, CuDNN 7.3 and PyTorch 1.0 on Ubuntu 18.04.

Citation

If you find this Model & Software useful in your research we would kindly ask you to cite:

@inproceedings{PROX:2019,
  title = {Resolving {3D} Human Pose Ambiguities with {3D} Scene Constraints},
  author = {Hassan, Mohamed and Choutas, Vasileios and Tzionas, Dimitrios and Black, Michael J.},
  booktitle = {International Conference on Computer Vision},
  month = oct,
  year = {2019},
  url = {https://prox.is.tue.mpg.de},
  month_numeric = {10}
}

Acknowledgments

The code is based on the SMPLify-X code. The Chamfer Distance code is taken from 3d-CODED. We thank Jean-Claude Passy for managing the Mesh Packages and porting it to Python 3 and .

Contact

For questions, please contact [email protected].

For commercial licensing (and all related questions for business applications), please contact [email protected].