Awesome

Installation

Environment

Retrive the repo first, for the rest sections, we assume the ROOT_DIR is at DenseMutualAttention:

git clone --recursive https://github.com/rongakowang/DenseMutualAttention.git

Create a conda venv and install pytorch:

conda env create -n DMA python=3.8
conda activate DMA
conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=11.0 -c pytorch

Install base dependencies:

pip install -r requirements.txt

Install torch-sparse and torch-scatter seperately, as they need to strictly match the CUDA version:

pip install torch-sparse==0.6.8 torch-scatter=2.0.5 -f https://data.pyg.org/whl/torch-1.7.0+cu110.html

Install dex-ycb-toolkit:

cd cd data/DexYCB/dex-ycb-toolkit
pip install -e .

The above commands are tested in Ubuntu 20.04.

Datasets

Download HO3D v2 from the official site and DexYCB dataset from the official site. Then unzip the datasets to ./local_data and rename them as ho3d and dex-ycb respectively.

Download the MANO hand model and extract the pkl files under ./local_data/mano.

Finally, download the additional data and extract them under local_data. The data contains:

• Simplified mesh data for the YCB objects, under local_data/ho3d_simple and local_data/dex_simple.
• Preprocessing meta data for the DexYCB dataset, under local_data/dex-ycb/meta.

The ./local_data folder should have structure like:

    ├── ho3d
    │   ├── evaluation
    │   ├── evaluation.txt
    │   ├── train
    │   └── train.txt
    ├── dex-ycb
    │   ├── 20200709-subject-01
    │   ├── 20200813-subject-02
    │   ├── 20200820-subject-03
    │   ├── 20200903-subject-04
    │   ├── 20200908-subject-05
    │   ├── 20200918-subject-06
    │   ├── 20200928-subject-07
    │   ├── 20201002-subject-08
    │   ├── 20201015-subject-09
    │   ├── 20201022-subject-10
    │   ├── bop
    │   ├── calibration
    │   └── models
    │   └── meta
    ├── mano
    │   ├── MANO_LEFT.pkl
    │   ├── MANO_RIGHT.pkl
    ├── ho3d_simple
    ├── dex_simple

Evaluation

HO3Dv2

Download the pretrained model and run the below command to obtain the result in Table 1 in the paper, note that the FPS may vary on devices.

cd main
python test.py --gpu 0 --model_path '../pretrained_weights/snapshot_ho3d.pth.tar'

For the hand metrics, you need to submit the ho3d_preds/pred.zip to the HO3D v2 CodaLab Challenge. The detailed results can be found in here for the user ako123:

DexYCB

Download the pretrained model and run the below command to obtain the result in Table 2 in the paper.

cd main
python test_dexycb.py --gpu 0 --model_path '../pretrained_weights/snapshot_dexycb.pth.tar'

Citation

If you use the code for your research, please cite with the below:

@InProceedings{Wang_2023_WACV,
    author    = {Wang, Rong and Mao, Wei and Li, Hongdong},
    title     = {Interacting Hand-Object Pose Estimation via Dense Mutual Attention},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2023},
    pages     = {5735-5745}
}

Acknowledge

The codebase is adopted from the repo handAR and homan, please also check out and cite them if you find them useful.