3D Clothed Human Reconstruction in the Wild (ClothWild codes)

3D Clothed Human Reconstruction in the Wild,
Gyeongsik Moon, Hyeongjin Nam, Takaaki Shiratori, Kyoung Mu Lee,
European Conference on Computer Vision (ECCV), 2022

Installation

• We recommend you to use an Anaconda virtual environment. Install PyTorch >=1.8.0 and Python >= 3.7.0.
• Install Pytorch3d following here depending on your environment.
• Then, run sh requirements.sh. You should slightly change torchgeometry kernel code following here.

Quick demo

• Download the pre-trained weight from here and place it in demo folder.
• Prepare base_data folder following below Directory part.
• Prepare input.png and edit its bbox of demo/demo.py.
• Prepare SMPL parameter, as pose2pose_result.json. You can get the SMPL parameter by running the off-the-shelf method [code].
• Run python demo.py --gpu 0.

Directory

Refer to here.

Running ClothWild

Train

In the main/config.py, you can change datasets to use.

cd ${ROOT}/main
python train.py --gpu 0

Test

Place trained model at the output/model_dump and follow below.

To evaluate CD (Chamfer Distance) on 3DPW, run

cd ${ROOT}/main
python test.py --gpu 0 --test_epoch 7 --type cd

To evaluate BCC (Body-Cloth Correspondence) on MSCOCO, run

cd ${ROOT}/main
python test.py --gpu 0 --test_epoch 7 --type bcc

You can download the checkpoint trained on MSCOCO+DeepFashion2 from here.

Result

Refer to the paper's main manuscript and supplementary material for diverse qualitative results!

Chamfer Distance (CD)

Body-Cloth Correspondence (BCC)

Reference

@InProceedings{Moon_2022_ECCV_ClothWild,  
author = {Moon, Gyeongsik and Nam, Hyeongjin and Shiratori, Takaaki and Lee, Kyoung Mu},  
title = {3D Clothed Human Reconstruction in the Wild},  
booktitle = {European Conference on Computer Vision (ECCV)},  
year = {2022}  
}