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RenderIH

Official PyTorch implementation of "RenderIH: A large-scale synthetic dataset for 3D interacting hand pose estimation", ICCV 2023 Project website

Our dataset

RenderIH: Download from Google Drive: imgs, annotations, materials; or BaiduPan: imgs annotations. Untar the compressed files of imgs and annotations, then run step7 in rendering_code. Materials is used for generation process in previous steps in rendering.

Prequeries

download and unzip [misc.tar]. Register and download MANO data. Put MANO_LEFT.pkl and MANO_RIGHT.pkl in misc/mano After collecting the above necessary files, the directory structure of ./misc is expected as follows:

./misc
├── mano
│   └── MANO_LEFT.pkl
│   └── MANO_RIGHT.pkl
├── model
│   └── config.yaml
├── graph_left.pkl
├── graph_right.pkl
├── upsample.pkl
├── v_color.pkl

Requirements

torch1.12.1: pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113

pytorch3d: pip install fvcore iopath; pip install git+https://github.com/facebookresearch/pytorch3d.git@stable

opencv4.7:pip install opencv_python==4.7.0.72

manopth pip install git+https://github.com/hassony2/chumpy.git,pip install git+https://github.com/hassony2/manopth.git

"sdf" change AT_CHECK in multiperson/sdf/csrc/sdf_cuda.cpp to TORCH_CHECK

mmcv:pip install -U openmim,mim install mmcv numpy,tqdm,yacs==0.1.8,tensorboardX,scipy,imageio,matplotlib,scikit-image,manopth,timm,imgaug,fvcore,iopath

DATASET

INTERHAND2.6M

  1. Download InterHand2.6M dataset and unzip it. (Noted: we used the v1.0_5fps version and H+M subset for training and evaluating)

  2. Process the dataset by :

python utils/dataset_gen/interhand.py --data_path PATH_OF_INTERHAND2.6M --save_path ./interhand2.6m/ --gen_anno 1 python utils/dataset_gen/interhand.py --data_path ./interhand2.6m/ --gen_anno 0 Replace PATH_OF_INTERHAND2.6M with your own store path of InterHand2.6M dataset.

Tzionas Dataset

  1. Download Hand-Hand Interaction from the website, categories from Walking to Hugging (01.zip~07.zip). Moreover, download the mano annotations from MANO_fits.

  2. Process the dataset by: python utils/dataset_gen/tzionas_generation.py --mano_annot xxx --detection_path xxx --rgb_path xxx --output_path xxx

Pretrained model

model without syntheic data model with synthetic data

Training

python apps/train.py --gpu 0,1,2,3 change INTERHAND_PATH in utils/default.yaml to the dataset path

utils/default.yaml has some argments that can be tuned

Evaluation

INTERHAND2.6M

python apps/eval_interhand.py --model MODEL_PATH --data_path INTERHAND2.6M_PATH change MODEL_PATH to the pretrained model path, and INTERHAND2.6M_PATH to dataset path.

Miscellaneous

data_type=0, dataset/interhand.py syn=True, use renderih together with Interhand2.6M

data_type=1, loader_ori using synthetic+real

data_type=2, loader.py using interhand_withother.py, training ego3dhand , h2o3d，or renderih

data_type=3, loader.py, using interhand_orisyn.py ，using the synthetic data

data_type=4, loader.py, using interhand_subset.py ，poseaug, subset synthetic and full real interhand data

utils/compute_maskiou.py. Calculate the iou distribution for hand data.

Citation

@inproceedings{li2023renderih,
  title={Renderih: A large-scale synthetic dataset for 3d interacting hand pose estimation},
  author={Li, Lijun and Tian, Linrui and Zhang, Xindi and Wang, Qi and Zhang, Bang and Bo, Liefeng and Liu, Mengyuan and Chen, Chen},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={20395--20405},
  year={2023}
}