Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation [3DV 2021 Oral]

Related tags

Deep Learningdigit-interacting
Overview

Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation [3DV 2021 Oral]

report report

Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation,
Zicong Fan, Adrian Spurr, Muhammed Kocabas, Siyu Tang, Michael J. Black, Otmar Hilliges International Conference on 3D Vision (3DV), 2021

Image

Features

DIGIT estimates the 3D poses of two interacting hands from a single RGB image. This repo provides the training, evaluation, and demo code for the project in PyTorch Lightning.

Updates

  • November 25 2021: Initial repo with training and evaluation on PyTorch Lightning 0.9.

Setting up environment

DIGIT has been implemented and tested on Ubuntu 18.04 with python >= 3.7, PyTorch Lightning 0.9 and PyTorch 1.6.

Clone the repo:

git clone https://github.com/zc-alexfan/digit-interacting

Create folders needed:

make folders

Install conda environment:

conda create -n digit python=3.7
conda deactivate
conda activate digit
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch
pip install -r requirements.txt

Downloading InterHand2.6M

  • Download the 5fps.v1 of InterHand2.6M, following the instructions here
  • Place annotations, images, and rootnet_output from InterHand2.6M under ./data/InterHand/*:
./data/InterHand
├── annotations
├── images
│   ├── test
│   ├── train
│   └── val
├── rootnet_output
│   ├── rootnet_interhand2.6m_output_all_test.json
│   └── rootnet_interhand2.6m_output_machine_annot_val.json
|-- annotations
|-- images
|   |-- test
|   |-- train
|   `-- val
`-- rootnet_output
    |-- rootnet_interhand2.6m_output_test.json
    `-- rootnet_interhand2.6m_output_val.json
  • The folder ./data/InterHand/annotations should look like this:
./data/InterHand/annotations
|-- skeleton.txt
|-- subject.txt
|-- test
|   |-- InterHand2.6M_test_MANO_NeuralAnnot.json
|   |-- InterHand2.6M_test_camera.json
|   |-- InterHand2.6M_test_data.json
|   `-- InterHand2.6M_test_joint_3d.json
|-- train
|   |-- InterHand2.6M_train_MANO_NeuralAnnot.json
|   |-- InterHand2.6M_train_camera.json
|   |-- InterHand2.6M_train_data.json
|   `-- InterHand2.6M_train_joint_3d.json
`-- val
    |-- InterHand2.6M_val_MANO_NeuralAnnot.json
    |-- InterHand2.6M_val_camera.json
    |-- InterHand2.6M_val_data.json
    `-- InterHand2.6M_val_joint_3d.json

Preparing data and backbone for training

Download the ImageNet-pretrained backbone from here and place it under:

./saved_models/pytorch/imagenet/hrnet_w32-36af842e.pt

Package images into lmdb:

cd scripts
python package_images_lmdb.py

Preprocess annotation:

python preprocess_annot.py

Render part segmentation masks:

  • Following the README.md of render_mano_ih to prepare an LMDB of part segmentation. For question in preparing the segmentation masks, please keep issues in there.

Place the LMDB from the images, the segmentation masks, and meta_dict_*.pkl to ./data/InterHand and it should look like the structure below. The cache files meta_dict_*.pkl are by-products of the step above.

|-- annotations
|   |-- skeleton.txt
|   |-- subject.txt
|   |-- test
|   |   |-- InterHand2.6M_test_MANO_NeuralAnnot.json
|   |   |-- InterHand2.6M_test_camera.json
|   |   |-- InterHand2.6M_test_data.json
|   |   |-- InterHand2.6M_test_data.pkl
|   |   `-- InterHand2.6M_test_joint_3d.json
|   |-- train
|   |   |-- InterHand2.6M_train_MANO_NeuralAnnot.json
|   |   |-- InterHand2.6M_train_camera.json
|   |   |-- InterHand2.6M_train_data.json
|   |   |-- InterHand2.6M_train_data.pkl
|   |   `-- InterHand2.6M_train_joint_3d.json
|   `-- val
|       |-- InterHand2.6M_val_MANO_NeuralAnnot.json
|       |-- InterHand2.6M_val_camera.json
|       |-- InterHand2.6M_val_data.json
|       |-- InterHand2.6M_val_data.pkl
|       `-- InterHand2.6M_val_joint_3d.json
|-- cache
|   |-- meta_dict_test.pkl
|   |-- meta_dict_train.pkl
|   `-- meta_dict_val.pkl
|-- images
|   |-- test
|   |-- train
|   `-- val
|-- rootnet_output
|   |-- rootnet_interhand2.6m_output_test.json
|   `-- rootnet_interhand2.6m_output_val.json
`-- segm_32.lmdb

Training and evaluating

To train DIGIT, run the command below. The script runs at a batch size of 64 using accumulated gradient where each iteration is on a batch size 32:

python train.py --iter_batch 32 --batch_size 64 --gpu_ids 0 --trainsplit train --precision 16 --eval_every_epoch 2 --lr_dec_epoch 40 --max_epoch 50 --min_epoch 50

OR if you just want to do a sanity check you can run:

python train.py --iter_batch 32 --batch_size 64 --gpu_ids 0 --trainsplit minitrain --valsplit minival --precision 16 --eval_every_epoch 1 --max_epoch 50 --min_epoch 50

Each time you run train.py, it will create a new experiment under logs and each experiment is assigned a key.

Supposed your experiment key is 2e8c5136b, you can evaluate the last epoch of the model on the test set by:

python test.py --eval_on minitest --load_ckpt logs/2e8c5136b/model_dump/last.ckpt

OR

python test.py --eval_on test --load_ckpt logs/2e8c5136b/model_dump/last.ckpt

The former only does the evaluation 1000 images for a sanity check.

Similarly, you can evaluate on the validation set:

python test.py --eval_on val --load_ckpt logs/2e8c5136b/model_dump/last.ckpt

Visualizing and evaluating pre-trained DIGIT

Here we provide instructions to show qualitative results of DIGIT.

Download pre-trained DIGIT:

wget https://dataset.ait.ethz.ch/downloads/dE6qPPePCV/db7cba8c1.pt
mv db7cba8c1.pt saved_models

Visualize results:

CUDA_VISIBLE_DEVICES=0 python demo.py --eval_on minival --load_from saved_models/db7cba8c1.pt  --num_workers 0

Evaluate pre-trained digit:

CUDA_VISIBLE_DEVICES=0 python test.py --eval_on test --load_from saved_models/db7cba8c1.pt --precision 16
CUDA_VISIBLE_DEVICES=0 python test.py --eval_on val --load_from saved_models/db7cba8c1.pt --precision 16

You should have the same results as in here.

The results will be dumped to ./visualization.

Citation

@inProceedings{fan2021digit,
  title={Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-pixel Part Segmentation},
  author={Fan, Zicong and Spurr, Adrian and Kocabas, Muhammed and Tang, Siyu and Black, Michael and Hilliges, Otmar},
  booktitle={International Conference on 3D Vision (3DV)},
  year={2021}
}

License

Since our code is developed based on InterHand2.6M, which is CC-BY-NC 4.0 licensed, the same LICENSE is applied to DIGIT.

DIGIT is CC-BY-NC 4.0 licensed, as found in the LICENSE file.

References

Some code in our repo uses snippets of the following repo:

Please consider citing them if you find our code useful:

@inproceedings{Moon_2020_ECCV_InterHand2.6M,  
author = {Moon, Gyeongsik and Yu, Shoou-I and Wen, He and Shiratori, Takaaki and Lee, Kyoung Mu},  
title = {InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image},  
booktitle = {European Conference on Computer Vision (ECCV)},  
year = {2020}  
}  

@inproceedings{sun2019deep,
  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={CVPR},
  year={2019}
}

@inproceedings{xiao2018simple,
    author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
    title={Simple Baselines for Human Pose Estimation and Tracking},
    booktitle = {European Conference on Computer Vision (ECCV)},
    year = {2018}
}

@misc{Charles2013,
  author = {milesial},
  title = {Pytorch-UNet},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/milesial/Pytorch-UNet}}
}

Contact

For any question, you can contact [email protected].

Owner
Zicong Fan
A Ph.D. student at ETH Zurich.
Zicong Fan
GitHub Repository
Time Dependent DFT in Tamm-Dancoff Approximation

Density Function Theory Program - kspy-tddft(tda) This is an implementation of Time-Dependent Density Functional Theory(TDDFT) using the Tamm-Dancoff

Peter Borthwick 2 Nov 17, 2022
The code for "Deep Level Set for Box-supervised Instance Segmentation in Aerial Images".

Deep Levelset for Box-supervised Instance Segmentation in Aerial Images Wentong Li, Yijie Chen, Wenyu Liu, Jianke Zhu* Any questions or discussions ar

sunshine.lwt 112 Jan 05, 2023
Oscar and VinVL

Oscar: Object-Semantics Aligned Pre-training for Vision-and-Language Tasks VinVL: Revisiting Visual Representations in Vision-Language Models Updates

Microsoft 938 Dec 26, 2022
End-to-end speech secognition toolkit

End-to-end speech secognition toolkit This is an E2E ASR toolkit modified from Espnet1 (version 0.9.9). This is the official implementation of paper:

Jinchuan Tian 147 Dec 28, 2022
A library for low-memory inferencing in PyTorch.

Pylomin Pylomin (PYtorch LOw-Memory INference) is a library for low-memory inferencing in PyTorch. Installation ... Usage For example, the following c

3 Oct 26, 2022
Implementation for "Seamless Manga Inpainting with Semantics Awareness" (SIGGRAPH 2021 issue)

Seamless Manga Inpainting with Semantics Awareness [SIGGRAPH 2021](To appear) | Project Website | BibTex Introduction: Manga inpainting fills up the d

101 Jan 01, 2023
[内测中]前向式Python环境快捷封装工具，快速将Python打包为EXE并添加CUDA、NoAVX等支持。

QPT - Quick packaging tool 快捷封装工具 GitHub主页 | Gitee主页 QPT是一款可以“模拟”开发环境的多功能封装工具，最短只需一行命令即可将普通的Python脚本打包成EXE可执行程序，并选择性添加CUDA和NoAVX的支持，尽可能兼容更多的用户环境。 感觉还可

QPT Family 545 Dec 28, 2022
Official code of ICCV2021 paper "Residual Attention: A Simple but Effective Method for Multi-Label Recognition"

CSRA This is the official code of ICCV 2021 paper: Residual Attention: A Simple But Effective Method for Multi-Label Recoginition Demo, Train and Vali

163 Dec 22, 2022
Language-Driven Semantic Segmentation

Language-driven Semantic Segmentation (LSeg) The repo contains official PyTorch Implementation of paper Language-driven Semantic Segmentation. Authors

Intelligent Systems Lab Org 416 Jan 03, 2023
The "breathing k-means" algorithm with datasets and example notebooks

The Breathing K-Means Algorithm (with examples) The Breathing K-Means is an approximation algorithm for the k-means problem that (on average) is bette

Bernd Fritzke 75 Nov 17, 2022
Improving Factual Completeness and Consistency of Image-to-text Radiology Report Generation

Improving Factual Completeness and Consistency of Image-to-text Radiology Report Generation The reference code of Improving Factual Completeness and C

46 Dec 15, 2022
PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning

Learning to Reweight Examples for Robust Deep Learning Unofficial PyTorch implementation of Learning to Reweight Examples for Robust Deep Learning. Th

Daniel Stanley Tan 325 Dec 28, 2022
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning (NeurIPS 2020) Introduction AdaShare is a novel and differentiable approach fo

94 Dec 22, 2022
FuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space OptimizationFuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space Optimization

FuseDream This repo contains code for our paper (paper link): FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimizat

XCL 191 Dec 31, 2022
Official implementation for the paper "Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection"

Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection PyTorch code release of the paper "Attentive Prototypes for Sour

Deepti Hegde 23 Oct 17, 2022
Code release of paper Improving neural implicit surfaces geometry with patch warping

NeuralWarp: Improving neural implicit surfaces geometry with patch warping Project page | Paper Code release of paper Improving neural implicit surfac

François Darmon 167 Dec 30, 2022
A Python library for Deep Probabilistic Modeling

Abstract DeeProb-kit is a Python library that implements deep probabilistic models such as various kinds of Sum-Product Networks, Normalizing Flows an

DeeProb-org 46 Dec 26, 2022
BED: A Real-Time Object Detection System for Edge Devices

BED: A Real-Time Object Detection System for Edge Devices About this project Thi

Data Analytics Lab at Texas A&M University 44 Nov 18, 2022
This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

OpenAI 3k Dec 26, 2022
This is a TensorFlow implementation for C2-Rec

This is a TensorFlow implementation for C2-Rec We refer to the repo SASRec. Requirements requirement.txt Datasets This repo includes Amazon Beauty dat

7 Nov 14, 2022