Official implementation of paper:

Scene-aware Egocentric 3D Human Pose Estimation

Jian Wang, Diogo Luvizon, Weipeng Xu, Lingjie Liu, Kripasindhu Sarkar, Christian Theobalt

CVPR 2023

1. Create a new anaconda environment

conda create -n sceneego python=3.9

conda activate sceneego

2. Install pytorch 1.13.1 from https://pytorch.org/get-started/previous-versions/

3. Install other dependencies

pip install -r requirements.txt

1. Download pre-trained pose estimation model and put it under models/sceneego/checkpoints

2. run:

python demo.py --config experiments/sceneego/test/sceneego.yaml --img_dir data/demo/imgs --depth_dir data/demo/depths --output_dir data/demo/out --vis True

The result will be shown with the open3d visualizer and the predicted pose is saved at data/demo/out.

3. The predicted pose is saved as the pkl file (e.g. img_001000.jpg.pkl). To visualize the predicted result, run:

python visualize.py --img_path data/demo/imgs/img_001000.jpg --depth_path data/demo/depths/img_001000.jpg.exr --pose_path data/demo/out/img_001000.jpg.pkl

The result will be shown with the open3d visualizer.