VisDB: Visibility-aware Dense Body

Introduction

This repo contains the PyTorch implementation of "Learning Visibility for Robust Dense Human Body Estimation" (ECCV'2022). Extended from a heatmap-based representation in I2L-MeshNet, we explicitly model the dense visibility of human joints and vertices to improve the robustness on partial-body images.

Example Results

We show some qualitative comparisons against prior arts which can handle occlusions: PARE and METRO. We observe that PARE and METRO are robust to occlusions in general but VisDB aligns with the images better thanks to the accurate dense heatmap and visibility estimations.

Setup

We implement VisDB with Python 3.7.10 and PyTorch 1.8.1. Our code is mainly built upon this repo: I2L-MeshNet.

• Install PyTorch and Python >= 3.7.3 and run sh requirements.sh.
• Change torchgeometry kernel code slightly following here.
• Download the pre-trained VisDB models (available soon).
• Download basicModel_f_lbs_10_207_0_v1.0.0.pkl and basicModel_m_lbs_10_207_0_v1.0.0.pkl from here and basicModel_neutral_lbs_10_207_0_v1.0.0.pkl from here. Place them under common/utils/smplpytorch/smplpytorch/native/models/.
• Download DensePose UV files UV_Processed.mat and UV_symmetry_transforms.mat from here and place them under common/.
• Download GMM prior gmm_08.pkl from here and place it under common/.

Quick demo

• Prepare input.jpg and pre-trained snapshot in the demo/ folder.
• Go to demo folder and edit bbox in demo.py.
• Run python demo.py --gpu 0 --stage param --test_epoch 8 if you want to run on gpu 0.
• Output images can be found under demo/.
• If you run this code in ssh environment without display device, do follow:

1、Install oemesa follow https://pyrender.readthedocs.io/en/latest/install/
2、Reinstall the specific pyopengl fork: https://github.com/mmatl/pyopengl
3、Set opengl's backend to egl or osmesa via os.environ["PYOPENGL_PLATFORM"] = "egl"

Usage

Data preparation

• Download training and testing data (MSCOCO, MuCo, Human3.6M, and 3DPW) following I2L-MeshNet's instructions.
• Run DensePose on images following here and store the UV estimations in individual data directories (e.g. ROOT/data/PW3D/dp/).

Training

1. lixel stage

First, you need to train VisDB in lixel stage. In the main/ folder, run

python train.py --gpu 0-3 --stage lixel 

to train the lixel model on GPUs 0-3.

2. param stage

Once you pre-trained VisDB in lixel stage, you can resume training in param stage. In the main/ folder, run

python train.py --gpu 0-3 --stage param --continue

to train the param model on GPUs 0-3.

Testing

Place trained model at the output/model_dump/. Choose the stage you want to test among (lixel or param).

In the main/ folder, run

python test.py --gpu 0-3 --stage $STAGE --test_epoch 8 

Contact

Chun-Han Yao: cyao6@ucmerced.edu

Citation

If you find our project useful in your research, please consider citing:

@inproceedings{yao2022learning,
  title={Learning visibility for robust dense human body estimation},
  author={Yao, Chun-Han and Yang, Jimei and Ceylan, Duygu and Zhou, Yi and Zhou, Yang, and Yang, Ming-Hsuan},
  booktitle={European conference on computer vision (ECCV)},
  year={2022}
}