Human Performance Capture from Monocular Video in the Wild (3DV2021)
Official code release for 3DV 2021 paper Human Performance Capture from Monocular Video in the Wild. We propose a method capable of capturing the dynamic 3D human shape from a monocular video featuring challenging body poses, without any additional input.
If you find our code or paper useful, please cite as
@inproceedings{guo2021human,
title={Human Performance Capture from Monocular Video in the Wild},
author={Guo, Chen and Chen, Xu and Song, Jie and Hilliges, Otmar},
booktitle={2021 International Conference on 3D Vision (3DV)},
pages={889--898},
year={2021},
organization={IEEE}
}
CLone this repo:
git clone https://github.com/MoyGcc/hpcwild.git
cd hpcwild
conda env create -f environment.yml
conda activate hpcwild
Additional Dependencies:
Download SMPL models (1.0.0 for Python 2.7 (10 shape PCs)) and move them to the corresponding places:
mkdir lib/smpl/smpl_model/
mv /path/to/smpl/models/basicModel_f_lbs_10_207_0_v1.0.0.pkl smpl_rendering/smpl_model/SMPL_FEMALE.pkl
mv /path/to/smpl/models/basicmodel_m_lbs_10_207_0_v1.0.0.pkl smpl_rendering/smpl_model/SMPL_MALE.pkl
Download checkpoints for external modules:
wget https://download.01.org/opencv/openvino_training_extensions/models/human_pose_estimation/checkpoint_iter_370000.pth
mv /path/to/checkpoint_iter_370000.pth external/lightweight-human-pose-estimation.pytorch/checkpoint_iter_370000.pth
wget https://dl.fbaipublicfiles.com/pifuhd/checkpoints/pifuhd.pt pifuhd.pt
mv /path/to/pifuhd.pt external/pifuhd/checkpoints/pifuhd.pt
Download IPNet weights: https://datasets.d2.mpi-inf.mpg.de/IPNet2020/IPNet_p5000_01_exp_id01.zip
unzip IPNet_p5000_01_exp_id01.zip
mv /path/to/IPNet_p5000_01_exp_id01 registration/experiments/IPNet_p5000_01_exp_id01
gdown --id 1mcr7ALciuAsHCpLnrtG_eop5-EYhbCmz -O modnet_photographic_portrait_matting.ckpt
mv /path/to/modnet_photographic_portrait_matting.ckpt external/MODNet/pretrained/modnet_photographic_portrait_matting.ckpt
Download 3DPW dataset
dataset_path
in test.conf
.bash mesh_recon.sh
to obtain the rigid body shape.bash registration.sh
to register a SMPL+D model to the rigid human body.bash tracking.sh
to capture the human performance temporally.We use the code in PIFuHD for the rigid body construction and adapt IPNet for human model registration. We use off-the-shelf methods OpenPose and MODNet for the extraction of 2D keypoints and sihouettes. We sincerely thank these authors for their awesome work.