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CPF: Learning a Contact Potential Field to Model the Hand-Object Interaction

Lixin Yang · Xinyu Zhan · Kailin Li · Wenqiang Xu · Jiefeng Li · Cewu Lu

ICCV 2021 & TPAMI 2024

ICCV TPAMI Video

Logo

This repo contains model, demo, training and test code.

Table of Contents
  1. Installation
  2. Evaluation
  3. Training
  4. Visualization
  5. A-MANO
  6. Citation

Installation

Following the Instruction to setup environment, assets, datasets and models.

 

Evaluation

The evaluation process is divided into two steps:

  1. dump the results from the hand-obj pose estimation (HoNet) and contact recovery (PiCR)
  2. optimize the poses using the contact based on grasping energy optimizer (GEO)

We provide a backup of our optimzation result at here.
The socres of following evaluations are provided in docs/scores.

fphab

# (optional) visualize the pose and contact prediction: 
# the drawings are saved in `tmp/draw/honet+picr/fphab` 
$ python scripts/eval.py --cfg config/HONetPiCRPipeline_fphab.yml -g 0 -b 1 --callback draw 


# 1. dump the pose and contact predictions:
# the predictions are saved in `tmp/pred/honet+picr/fphab/dumped_pred`
$ python scripts/eval.py --cfg config/HONetPiCRPipeline_fphab.yml -g 0 -b 1 --callback dump


# 2. after dumping the results, fit pose using the grasp energy optimizer (GEO):
# the --data_prefix is the directory where dumped predictions are saved
# the optimized results are saved in `tmp/pred/honet+picr/fphab/optmized`
$ python scripts/fit.py --cfg config/HONetPiCRPipeline_fphab.yml -g 0,1,2,3 -w 16 \
        --data_prefix tmp/pred/honet+picr/fphab/dumped_pred

HO3Dv2

@note: HO3Dv2 will use the hand-obj pose estimation network in ArtiBoost.
Download the HO3Dv2 test set result at here, and put it in this porject as
assets/eval_ho3dv2_clasbased_artiboost_iknet_SUBMIT.pkl

# (optional) visualize the pose and contact prediction: 
# the drawings are saved in `tmp/draw/artiboost+picr/ho3dv2`
$ python scripts/eval.py --cfg config/HOPose_PiCRPipeline_ho3dv2.yml -g 0 -b 1 --callback draw 


# 1. dump the pose and contact predictions (pose is from ArtiBoost model, contact is from PiCR):
# the predictions are saved in `tmp/pred/artiboost+picr/ho3dv2/dumped_pred` 
$ python scripts/eval.py --cfg config/HOPose_PiCRPipeline_ho3dv2.yml -g 0 -b 1 --callback dump 


# 2. after dumping the results, fit pose using the grasp energy optimizer (GEO):
$ python scripts/fit.py -c config/HOPose_PiCRPipeline_ho3dv2.yml -g 0,1,2 -w 12 \
        --data_prefix tmp/pred/artiboost+picr/ho3dv2/dumped_pred  \
        --lambda_repulsion_loss 2.0

e.g. HO pose and contact prediction in ho3dv2 test set capture capture

visulize the optimization process using flag runtime_viz. e.g. python scripts/fit.py ... --runtime_viz

capture

 

Training

@note: We only provide the training code for the contact recovery network (PiCR).
For training the Hand-Object Pose Etimation (HOPE) network, please refer to handobjectconsist, or Semi-Hand-Object or ArtiBoost.

PiCR on fphab

$ python scripts/train_ddp.py --cfg config/train_PiCR_fphab.yml  -g 0,1 -b 48 -w 1 --eval_freq 100

PiCR on HO3Dv2

$ python scripts/train_ddp.py --cfg config/train_PiCR_ho3d_mix.yml -g 0,1 -b 48 -w 4 --eval_freq 100

The results are saved at exp/defualt_{exp_time}/checkpoints/checkpoint/PiCRI.pth.tar)
Once the traininig is done, you can modify the MODEL.PICR.PRETRAINED value in config/xxxxPipeline_{dataset}.yml to the saved checkpoint path, and evaluate the model again.

 

Visualization

We provide a visualization tool to visualize the contact dataset, and a tool to create the contact annotation from existing dataset. Please refer to docs/contact_dataset.

 

A-MANO

We provide a pytorch MANO implementation: manotorch that provides the basic MANO layer: $V, J = \mathcal{M}(\theta, \beta)$, and features the 1) Anatomical Consistent Basis, 2) Anatomy Loss for pose correction, and a esay-to-use 3) Hand Pose Composition tool.

Anatomical Consistent Basis Anatomy Loss Hand Pose Composition

 

Citation

If you find this work helpful, please consider citing us:

@inproceedings{yang2021cpf,
  author={Yang, Lixin and Zhan, Xinyu and Li, Kailin and Xu, Wenqiang and Li, Jiefeng and Lu, Cewu},
  title={{CPF}: Learning a Contact Potential Field To Model the Hand-Object Interaction},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  month={October},
  year={2021},
  pages={11097-11106}
}

@artical{yang2024learning,
  author={Yang, Lixin and Zhan, Xinyu and Li, Kailin and Xu, Wenqiang and Zhang, Junming and Li, Jiefeng and Lu, Cewu},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Learning a Contact Potential Field for Modeling the Hand-Object Interaction}, 
  year={2024},
  volume={46},
  number={8},
  pages={5645-5662},
  keywords={Pose estimation;Task analysis;Grasping;Three-dimensional displays;Contacts;Semantics;Image reconstruction;Contact modeling;grasping pose generation;hand-object pose estimation},
  doi={10.1109/TPAMI.2024.3372102}
}