Reconstructing Hand-Held Objects from Monocular Video
Di Huang, Xiaopeng Ji, Xingyi He, Jiaming Sun, Tong He, Qing Shuai, Wanli Ouyang, Xiaowei Zhou
Siggraph Asia 2022 Conference Track

• 2023/06/19: The hand tracking code is released here: EasyMocap. Note that this hand tracking version is slightly different with the original hand tracking code.
• 2023/02/13: For people who do not want to run hand tracking, we provide the processed hand tracking results: HOD_S1_HT and HOD_D1_HT. HOD_S1_HT use the same hand tracking algorithm as the paper (sharing hand parameters), while HOD_D1_HT does not share hand parameters.

• Release the minimal runnable code.
• Release the hand tracking code.
• Release the dense reconstruction code.
• Release the dataset.

Note: The hand tracking code will be released with the EasyMocap repo.

conda create -n hhorv1 python=3.7

# PyTorch
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch

# requirements
pip install -r requirements.txt

We use the following 3rd party libraries and directly add their code to this repo(NanoDet, EasyMocap, BackgroundMattingV2, MMSegmentation). Our dense reconstruction code is built upon NeuS. Thanks for their great work and we appreciate their contribution to the community. Please follow their installation instructions and LICENSE.

# NanoDet (for detection during hand tracking)
cd nanodet
python setup.py develop --user

# EasyMocap (for hand tracking)
cd EasyMocap
python setup.py develop --user

# Background Matting (for foreground segmentation)

# MMSegmentation (for hand segmentation)
cd mmsegmentation
pip install mmcv-full==1.4.0
python setup.py develop --user

• Download the pretrained hand segmentation model here and unzip to mmsegmentation/work_dirs.
• Download the pretrained hand detection model here and unzip it to nanodet/data.

• Download the demo data here and unzip to data/DEMO.

# Edit 0_set_path.sh
vim apps/0_set_path.sh

# Prepare video to images (done in the demo data)
# sh apps/1_video_to_imgs.sh

# Run hand tracking (done in the demo data)
# sh apps/2_run_easymocap.sh

# Run mask generation
sh apps/3_background_matting.sh

# Generate camera poses by MANO
sh apps/4_get_mano_camera.sh

# Run hand segmentation
sh apps/5_run_segmentation.sh

# Dense reconstruction 
cd NeuS
vim confs/demo/hand_scanner_wmask_wbarf_wsem_warp10.0_mask5_demo.conf  # Edit the path to the demo data
CUDA_VISIBLE_DEVICES=0 python exp_runner.py --mode train --conf ./confs/demo/hand_scanner_wmask_wbarf_wsem_warp10.0_mask5_demo.conf --case demo
CUDA_VISIBLE_DEVICES=0 python exp_runner.py --mode validate_mesh --conf ./confs/demo/hand_scanner_wmask_wbarf_wsem_warp10.0_mask5_demo.conf --case demo --is_continue

After running the above commands, you can find the reconstructed mesh in NeuS/exp/demo/handscanner/meshes/00100000_org.ply and NeuS/exp/demo/handscanner/meshes/00100000_obj.ply. The *_org.ply is the reconstructed mesh with the hand and the *_obj.ply is the reconstructed mesh without the hand. For 00100000_obj.ply, you can use MeshLab to remove the useless parts (e.g., the remaining hand) and complete the hole with Poisson reconstruction or hole_fixer.

• HOD_S1 is the main dataset used in the paper, which contains hand-held objects with fixed grasping poses.
• HOD_S2 and HOD_D1 are the datasets for future hand-held object reconstruction research. HOD_S2 has hand-held objects with different fixed grasping poses than HOD_S1. HOD_D1 has hand-held objects with dynamic graspings. Check our paper for more details.

@inproceedings{huang2022hhor,
  title={Reconstructing Hand-Held Objects from Monocular Video},
  author={Huang, Di and Ji, Xiaopeng and He, Xingyi and Sun, Jiaming and He, Tong and Shuai, Qing and Ouyang, Wanli and Zhou, Xiaowei},
  booktitle={SIGGRAPH Asia Conference Proceedings},
  year={2022},
}