InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image

Introduction

This repo is official PyTorch implementation of InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image (ECCV 2020).
Our InterHand2.6M dataset is the first large-scale real-captured dataset with accurate GT 3D interacting hand poses.

Above demo videos have low-quality frames because of the compression for the README upload.

News

2020.11.26. Demo code for a random image is added! Checkout below instructions.
2020.11.26. Fitted MANO parameters are updated to the better ones (fitting error is about 5 mm). Also, reduced to much smaller file size by providing parameters fitted to the world coordinates (independent on the camera view).
2020.10.7. Fitted MANO parameters are available! They are obtained by NeuralAnnot.

InterHand2.6M dataset

For the InterHand2.6M dataset download and instructions, go to [HOMEPAGE].
Full images in 5 fps and 30 fps are under inspection of FB to prevent privacy leak (e.g., fingerprint and face of the subjects). It is taking much longer time than we expected (maybe due to the COVID)... Sorry for the late release 😭😭😭
Belows are instructions for our baseline model, InterNet, for 3D interacting hand pose estimation from a single RGB image.

Demo on a random image

Download pre-trained InterNet from here
Put the model at demo folder
Go to demo folder and edit bbox in here
run python demo.py --gpu 0 --test_epoch 20
You can see result_2D.jpg and 3D viewer.

MANO mesh rendering demo

Install SMPLX
cd MANO_render
Set smplx_path and root_path in render.py
Run python render.py

Directory

Root

The ${ROOT} is described as below.

${ROOT}
|-- data
|-- common
|-- main
|-- output

data contains data loading codes and soft links to images and annotations directories.
common contains kernel codes for 3D interacting hand pose estimation.
main contains high-level codes for training or testing the network.
output contains log, trained models, visualized outputs, and test result.

Data

You need to follow directory structure of the data as below.

${ROOT}
|-- data
|   |-- STB
|   |   |-- data
|   |   |-- rootnet_output
|   |   |   |-- rootnet_stb_output.json
|   |-- RHD
|   |   |-- data
|   |   |-- rootnet_output
|   |   |   |-- rootnet_rhd_output.json
|   |-- InterHand2.6M
|   |   |-- annotations
|   |   |   |-- all
|   |   |   |-- human_annot
|   |   |   |-- machine_annot
|   |   |-- images
|   |   |   |-- train
|   |   |   |-- val
|   |   |   |-- test
|   |   |-- rootnet_output
|   |   |   |-- rootnet_interhand2.6m_output_all_test.json
|   |   |   |-- rootnet_interhand2.6m_output_machine_annot_val.json

Download InterHand2.6M data [HOMEPAGE]
Download STB parsed data [images] [annotations]
Download RHD parsed data [images] [annotations]
All annotation files follow MS COCO format.
If you want to add your own dataset, you have to convert it to MS COCO format.

Output

You need to follow the directory structure of the output folder as below.

${ROOT}
|-- output
|   |-- log
|   |-- model_dump
|   |-- result
|   |-- vis

log folder contains training log file.
model_dump folder contains saved checkpoints for each epoch.
result folder contains final estimation files generated in the testing stage.
vis folder contains visualized results.

Running InterNet

Start

In the main/config.py, you can change settings of the model including dataset to use and which root joint translation vector to use (from gt or from RootNet).

Train

In the main folder, run

python train.py --gpu 0-3 --annot_subset $SUBSET

to train the network on the GPU 0,1,2,3. --gpu 0,1,2,3 can be used instead of --gpu 0-3. If you want to continue experiment, run use --continue.

$SUBSET is one of [all, human_annot, machine_annot].

all: Combination of the human and machine annotation. Train (H+M) in the paper.
human_annot: The human annotation. Train (H) in the paper.
machine_annot: The machine annotation. Train (M) in the paper.

Test

Place trained model at the output/model_dump/.

In the main folder, run

python test.py --gpu 0-3 --test_epoch 20 --test_set $DB_SPLIT --annot_subset $SUBSET

to test the network on the GPU 0,1,2,3 with snapshot_20.pth.tar. --gpu 0,1,2,3 can be used instead of --gpu 0-3.

$DB_SPLIT is one of [val,test].

val: The validation set. Val in the paper.
test: The test set. Test in the paper.