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

Related tags

Deep LearningCPF
Overview

Contact Potential Field

This repo contains model, demo, and test codes of our paper: CPF: Learning a Contact Potential Field to Model the Hand-object Interaction

Guide to the Demo

1. Get our code:

$ git clone --recursive https://github.com/lixiny/CPF.git
$ cd CPF

2. Set up your new environment:

$ conda env create -f environment.yaml
$ conda activate cpf

3. Download assets files and put it in assets folder.

Download the MANO model files from official MANO website, and put it into assets/mano. We currently only use the MANO_RIGHT.pkl

Now your assets folder should look like this:

.
├── anchor/
│   ├── anchor_mapping_path.pkl
│   ├── anchor_weight.txt
│   ├── face_vertex_idx.txt
│   └── merged_vertex_assignment.txt
├── closed_hand/
│   └── hand_mesh_close.obj
├── fhbhands_fits/
│   ├── Subject_1/
│   │   ├── ...
│   ├── Subject_2/
|   ├── ...
├── hand_palm_full.txt
└── mano/
    ├── fhb_skel_centeridx9.pkl
    ├── info.txt
    ├── LICENSE.txt
    └── MANO_RIGHT.pkl

4. Download Dataset

First-Person Hand Action Benchmark (fhb)

Download and unzip the First-Person Hand Action Benchmark dataset following the official instructions to the data/fhbhands folder If everything is correct, your data/fhbhands should look like this:

.
├── action_object_info.txt
├── action_sequences_normalized/
├── change_log.txt
├── data_split_action_recognition.txt
├── file_system.jpg
├── Hand_pose_annotation_v1/
├── Object_6D_pose_annotation_v1_1/
├── Object_models/
├── Subjects_info/
├── Video_files/
├── Video_files_480/ # Optionally

Optionally, resize the images (speeds up training !) based on the handobjectconsist/reduce_fphab.py.

$ python reduce_fphab.py

Download our fhbhands_supp and place it at data/fhbhands_supp:

Download our fhbhands_example and place it at data/fhbhands_example. This fhbhands_example contains 10 samples that are designed to demonstrate our pipeline.

├── fhbhands/
├── fhbhands_supp/
│   ├── Object_models/
│   └── Object_models_binvox/
├── fhbhands_example/
│   ├── annotations/
│   ├── images/
│   ├── object_models/
│   └── sample_list.txt

HO3D

Download and unzip the HO3D dataset following the official instructions to the data/HO3D folder. if everything is correct, the HO3D & YCB folder in your data should look like this:

data/
├── HO3D/
│   ├── evaluation/
│   ├── evaluation.txt
│   ├── train/
│   └── train.txt
├── YCB_models/
│   ├── 002_master_chef_can/
│   ├── ...

Download our YCB_models_supp and place it at data/YCB_models_supp

Now the data folder should have a root structure like:

data/
├── fhbhands/
├── fhbhands_supp/
├── fhbhands_example/
├── HO3D/
├── YCB_models/
├── YCB_models_supp/

5. Download pre-trained checkpoints

download our pre-trained CPF_checkpoints, unzip it at the CPF_checkpoints folder:

CPF_checkpoints/
├── honet/
│   ├── fhb/
│   ├── ho3dofficial/
│   └── ho3dv1/
├── picr/
│   ├── fhb/
│   ├── ho3dofficial/
│   └── ho3dv1/

6. Launch visualization

We create a FHBExample dataset in hocontact/hodatasets/fhb_example.py that only contains 10 samples to demonstrate our pipeline. Notice: this demo requires active screen for visualizing. Press q in the "runtime hand" window to start fitting.

$ python training/run_demo.py \
    --gpu 0 \
    --init_ckpt CPF_checkpoints/picr/fhb/checkpoint_200.pth.tar \
    --honet_mano_fhb_hand

7. Test on full dataset (FHB, HO3D v1/v2)

We provide shell srcipts to test on the full dataset to approximately reproduce our results.

FHB

dump the results of HoNet and PiCR:

$ python training/dumppicr_dist.py \
    --gpu 0,1 \
    --dist_master_addr localhost \
    --dist_master_port 12355 \
    --exp_keyword fhb \
    --train_datasets fhb \
    --train_splits train \
    --val_dataset fhb \
    --val_split test \
    --split_mode actions \
    --batch_size 8 \
    --dump_eval \
    --dump \
    --vertex_contact_thresh 0.8 \
    --filter_thresh 5.0 \
    --dump_prefix common/picr \
    --init_ckpt CPF_checkpoints/picr/fhb/checkpoint_200.pth.tar

and reload the GeO optimizer:

# setting 1: hand-only
$ CUDA_VISIBLE_DEVICES=0,1,2,3 python training/optimize.py \
    --n_workers 16 \
    --data_path common/picr/fhbhands/test_actions_mf1.0_rf0.25_fct5.0_ec \
    --mode hand

# setting 2: hand-obj
$ CUDA_VISIBLE_DEVICES=0,1,2,3 python training/optimize.py \
    --n_workers 16 \
    --data_path common/picr/fhbhands/test_actions_mf1.0_rf0.25_fct5.0_ec \
    --mode hand_obj \
    --compensate_tsl

HO3Dv1

dump:

$ python training/dumppicr_dist.py  \
    --gpu 0,1 \
    --dist_master_addr localhost \
    --dist_master_port 12356 \
    --exp_keyword ho3dv1 \
    --train_datasets ho3d \
    --train_splits train \
    --val_dataset ho3d \
    --val_split test \
    --split_mode objects \
    --batch_size 4 \
    --dump_eval \
    --dump \
    --vertex_contact_thresh 0.8 \
    --filter_thresh 5.0 \
    --dump_prefix common/picr_ho3dv1 \
    --init_ckpt CPF_checkpoints/picr/ho3dv1/checkpoint_300.pth.tar

and reload optimizer:

# hand-only
$ CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python training/optimize.py \
    --n_workers 24 \
    --data_path common/picr_ho3dv1/HO3D/test_objects_mf1_likev1_fct5.0_ec/ \
    --lr 1e-2 \
    --n_iter 500 \
    --hodata_no_use_cache \
    --lambda_contact_loss 10.0 \
    --lambda_repulsion_loss 4.0 \
    --repulsion_query 0.030 \
    --repulsion_threshold 0.080 \
    --mode hand

# hand-obj
$ CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python training/optimize.py \
    --n_workers 24 \
    --data_path common/picr_ho3dv1/HO3D/test_objects_mf1_likev1_fct5.0_ec/ \
    --lr 1e-2 \
    --n_iter 500  \
    --hodata_no_use_cache \
    --lambda_contact_loss 10.0 \
    --lambda_repulsion_loss 6.0 \
    --repulsion_query 0.030 \
    --repulsion_threshold 0.080 \
    --mode hand_obj

HO3Dofficial

dump:

$ python training/dumppicr_dist.py  \
    --gpu 0,1 \
    --dist_master_addr localhost \
    --dist_master_port 12356 \
    --exp_keyword ho3dofficial \
    --train_datasets ho3d \
    --train_splits val \
    --val_dataset ho3d \
    --val_split test \
    --split_mode official \
    --batch_size 4 \
    --dump_eval \
    --dump \
    --test_dump \
    --vertex_contact_thresh 0.8 \
    --filter_thresh 5.0 \
    --dump_prefix common/picr_ho3dofficial \
    --init_ckpt CPF_checkpoints/picr/ho3dofficial/checkpoint_300.pth.tar

and reload optimizer:

$ CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python training/optimize.py \
    --n_workers 24 \
    --data_path common/picr_ho3dofficial/HO3D/test_official_mf1_likev1_fct\(x\)_ec/  \
    --lr 1e-2 \
    --n_iter 500 \
    --hodata_no_use_cache \
    --lambda_contact_loss 10.0 \
    --lambda_repulsion_loss 2.0 \
    --repulsion_query 0.030 \
    --repulsion_threshold 0.080 \
    --mode hand_obj

Results

Testing on the full dataset may take a while ( 0.5 ~ 1.5 day ), thus we also provide our test results at fitting_res.txt.

K-MANO

We provide pytorch implementation of our Kinematic-chained MANO in lixiny/manopth, which is modified from the original hassony2/manopth. Thank Yana Hasson for providing the code.

Citation

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

@article{yang2020cpf,
  title={CPF: Learning a Contact Potential Field to Model the Hand-object Interaction},
  author={Yang, Lixin and Zhan, Xinyu and Li, Kailin and Xu, Wenqiang and Li, Jiefeng and Lu, Cewu},
  journal={arXiv preprint arXiv:2012.00924},
  year={2020}
}

And if you have any question or suggestion, do not hesitate to contact me through siriusyang[at]sjtu[dot]edu[dot]cn.

Comments
  • FileNotFoundError: [Errno 2] No such file or directory: 'assets/mano/MANO_RIGHT.pkl'

    FileNotFoundError: [Errno 2] No such file or directory: 'assets/mano/MANO_RIGHT.pkl'

    I executed this command: python training/run_demo.py --gpu 0 --init_ckpt CPF_checkpoints/picr/fhb/checkpoint_200.pth.tar --honet_mano_fhb_hand

    image

    So, I moved assets/mano folder to the path CPF/manopth/mano/webuser/ But, I am still getting the error

    opened by anjugopinath 3
  • AttributeError: 'ParsedRequirement' object has no attribute 'req'

    AttributeError: 'ParsedRequirement' object has no attribute 'req'

    Could you tell me which version of Anaconda to use please? I am getting the below error:

    neptune:/s/red/a/nobackup/vision/anju/CPF$ conda env create -f environment.yaml Collecting package metadata (repodata.json): done Solving environment: done

    ==> WARNING: A newer version of conda exists. <== current version: 4.9.2 latest version: 4.10.1

    Please update conda by running

    $ conda update -n base -c defaults conda

    Preparing transaction: done Verifying transaction: done Executing transaction: done Installing pip dependencies: | Ran pip subprocess with arguments: ['/s/chopin/a/grad/anju/.conda/envs/cpf/bin/python', '-m', 'pip', 'install', '-U', '-r', '/s/red/a/nobackup/vision/anju/CPF/condaenv.agtpjn0v.requirements.txt'] Pip subprocess output: Collecting git+https://github.com/utiasSTARS/liegroups.git (from -r /s/red/a/nobackup/vision/anju/CPF/condaenv.agtpjn0v.requirements.txt (line 1)) Cloning https://github.com/utiasSTARS/liegroups.git to /tmp/pip-req-build-ey_prxpa Obtaining file:///s/red/a/nobackup/vision/anju/CPF/manopth (from -r /s/red/a/nobackup/vision/anju/CPF/condaenv.agtpjn0v.requirements.txt (line 12)) Obtaining file:///s/red/a/nobackup/vision/anju/CPF (from -r /s/red/a/nobackup/vision/anju/CPF/condaenv.agtpjn0v.requirements.txt (line 13)) Collecting trimesh==3.8.10 Using cached trimesh-3.8.10-py3-none-any.whl (625 kB) Collecting open3d==0.10.0.0 Using cached open3d-0.10.0.0-cp38-cp38-manylinux1_x86_64.whl (4.7 MB) Collecting pyrender==0.1.43 Using cached pyrender-0.1.43-py3-none-any.whl (1.2 MB) Collecting scikit-learn==0.23.2 Using cached scikit_learn-0.23.2-cp38-cp38-manylinux1_x86_64.whl (6.8 MB) Collecting chumpy==0.69 Using cached chumpy-0.69.tar.gz (50 kB)

    Pip subprocess error: Running command git clone -q https://github.com/utiasSTARS/liegroups.git /tmp/pip-req-build-ey_prxpa ERROR: Command errored out with exit status 1: command: /s/chopin/a/grad/anju/.conda/envs/cpf/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-hnf78qhk/chumpy/setup.py'"'"'; file='"'"'/tmp/pip-install-hnf78qhk/chumpy/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' egg_info --egg-base /tmp/pip-pip-egg-info-k7bp5gq7 cwd: /tmp/pip-install-hnf78qhk/chumpy/ Complete output (7 lines): Traceback (most recent call last): File "", line 1, in File "/tmp/pip-install-hnf78qhk/chumpy/setup.py", line 15, in install_requires = [str(ir.req) for ir in install_reqs] File "/tmp/pip-install-hnf78qhk/chumpy/setup.py", line 15, in install_requires = [str(ir.req) for ir in install_reqs] AttributeError: 'ParsedRequirement' object has no attribute 'req' ---------------------------------------- ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.

    failed

    CondaEnvException: Pip failed

    opened by anjugopinath 3
  • How to use CPF on both hands?

    How to use CPF on both hands?

    Thanks a lot for your great work! I have a question: Since you only use the MANO_RIGHT.pkl, it seems that CPF currently can only construct right hand model, right? What is needed to be modified to use CPF on both hands? Thanks!

    opened by buaacyw 3
  • Error when executing command

    Error when executing command "conda env create -f environment.yaml"

    Hi,

    I get the below error when executing the command "conda env create -f environment.yaml"

    CondaError: Downloaded bytes did not match Content-Length url: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/linux-64/pytorch-1.6.0-py3.8_cuda10.2.89_cudnn7.6.5_0.tar.bz2 target_path: /home/anju/anaconda3/pkgs/pytorch-1.6.0-py3.8_cuda10.2.89_cudnn7.6.5_0.tar.bz2 Content-Length: 564734769 downloaded bytes: 221675180

    opened by anjugopinath 1
  • Some questions about PiQR code

    Some questions about PiQR code

    In the contacthead.py, the three decoders have different input dimension. self.vertex_contact_decoder = PointNetDecodeModule(self._concat_feat_dim, 1) self.contact_region_decoder = PointNetDecodeModule(self._concat_feat_dim + 1, self.n_region) self.anchor_elasti_decoder = PointNetDecodeModule(self._concat_feat_dim + 17, self.n_anchor)

    I am wondering if this part is used to predict selected anchor points within each subregion.

    The classification of subregions is obtained by contact_region_decoder and then the anchor points are predicted by anchor_elasti_decoder, is it right ?

    I am a little bit confused about it, because according to the paper, Anchor Elasticity (AE) represents the elasticities of the attractive springs. But in the code, the output of anchor_elasti_decoder has no relation to the elasticity parameter, I'm wondering if there's some part I've missed.

    Sorry for any trouble caused and thanks for your help!

    opened by lym29 0
  • what's the meaning of

    what's the meaning of "adapt"?

    I notice that there are hand_pose_axisang_adapt_np and hand_pose_axisang_np in your code. Could you please explain what's the difference between them?

    opened by Yamato-01 5
  • Expected code date ?

    Expected code date ?

    Hi !

    I just read through your paper, congratulation on the great work ! I love the fact that you provide an anatomically-constrained MANO, and the per-object-vertex hand part affinity.

    I look forward to the code realease :)

    Do you have a planned date in mind ?

    All the best,

    Yana

    opened by hassony2 4
Releases(v1.0.0)
Owner
Lixin YANG
PhD student @ SJTU. Computer Vision, Robotic Vision and Hand-obj Interaction
Lixin YANG
GitHub Repository
Continuous Query Decomposition for Complex Query Answering in Incomplete Knowledge Graphs

Continuous Query Decomposition This repository contains the official implementation for our ICLR 2021 (Oral) paper, Complex Query Answering with Neura

UCL Natural Language Processing 71 Dec 29, 2022
Neural network for digit classification powered by cuda

cuda_nn_mnist Neural network library for digit classification powered by cuda Resources The library was built to work with MNIST dataset. python-mnist

Nikita Ardashev 1 Dec 20, 2021
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

ENet in Caffe Execution times and hardware requirements Network 1024x512 1280x720 Parameters Model size (fp32) ENet 20.4 ms 32.9 ms 0.36 M 1.5 MB SegN

Timo Sämann 561 Jan 04, 2023
Code for Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games

Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games How to run our algorithm? Create the new environment using: conda

MARL @ SJTU 8 Dec 27, 2022
Pytorch implementation of "MOSNet: Deep Learning based Objective Assessment for Voice Conversion"

MOSNet pytorch implementation of "MOSNet: Deep Learning based Objective Assessment for Voice Conversion" https://arxiv.org/abs/1904.08352 Dependency L

9 Nov 18, 2022
CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement

CBREN This is the Pytorch implementation for our IEEE TCSVT paper : CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhanceme

Zhao Hengrun 3 Nov 04, 2022
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

Build Type Linux MacOS Windows Build Status OpenPose has represented the first real-time multi-person system to jointly detect human body, hand, facia

25.7k Jan 09, 2023
PyTorch Implement for Path Attention Graph Network

SPAGAN in PyTorch This is a PyTorch implementation of the paper "SPAGAN: Shortest Path Graph Attention Network" Prerequisites We prefer to create a ne

Yang Yiding 38 Dec 28, 2022
LeafSnap replicated using deep neural networks to test accuracy compared to traditional computer vision methods.

Deep-Leafsnap Convolutional Neural Networks have become largely popular in image tasks such as image classification recently largely due to to Krizhev

Sujith Vishwajith 48 Nov 27, 2022
Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

PackNet: https://arxiv.org/abs/1711.05769 Pretrained models are available here: https://uofi.box.com/s/zap2p03tnst9dfisad4u0sfupc0y1fxt Datasets in Py

Arun Mallya 216 Jan 05, 2023
Codes and models of NeurIPS2021 paper - DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks

DominoSearch This is repository for codes and models of NeurIPS2021 paper - DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense n

11 Sep 10, 2022
Image segmentation with private İstanbul Dataset

Image Segmentation This repo was created for academic research and test result. Repo will update after academic article online. This repo contains wei

İrem KÖMÜRCÜ 9 Dec 11, 2022
Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models.

Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models

AdvBox 1.3k Dec 25, 2022
A PyTorch Lightning Callback for pushing models to the Hugging Face Hub 🤗⚡️

hf-hub-lightning A callback for pushing lightning models to the Hugging Face Hub. Note: I made this package for myself, mostly...if folks seem to be i

Nathan Raw 27 Dec 14, 2022
DeepOBS: A Deep Learning Optimizer Benchmark Suite

DeepOBS - A Deep Learning Optimizer Benchmark Suite DeepOBS is a benchmarking suite that drastically simplifies, automates and improves the evaluation

Aaron Bahde 7 May 12, 2020
Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.

Milano (This is a research project, not an official NVIDIA product.) Documentation https://nvidia.github.io/Milano Milano (Machine learning autotuner

NVIDIA Corporation 147 Dec 17, 2022
[ICSE2020] MemLock: Memory Usage Guided Fuzzing

MemLock: Memory Usage Guided Fuzzing This repository provides the tool and the evaluation subjects for the paper "MemLock: Memory Usage Guided Fuzzing

Cheng Wen 54 Jan 07, 2023
A PyTorch-based library for fast prototyping and sharing of deep neural network models.

A PyTorch-based library for fast prototyping and sharing of deep neural network models.

78 Jan 03, 2023
Repository for Multimodal AutoML Benchmark

Benchmarking Multimodal AutoML for Tabular Data with Text Fields Repository for the NeurIPS 2021 Dataset Track Submission "Benchmarking Multimodal Aut

Xingjian Shi 44 Nov 24, 2022
Reimplementation of NeurIPS'19: "Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting" by Shu et al.

[Re] Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Reimplementation of NeurIPS'19: "Meta-Weight-Net: Learning an Explicit Mapping

Robert Cedergren 1 Mar 13, 2020