This repo is official PyTorch implementation of Bringing Inputs to Shared Domains for 3D Interacting Hands Recovery in the Wild (CVPR 2023).

1. Prepare human_model_files folder following below Directory part and place it at common/utils/human_model_files.
2. Move to demo folder.
3. Download pre-trained InterWild from here.
4. Put input images at images. The image should be a cropped image, which contain a single human. For example, using a human detector. We have a hand detection network, so no worry about the hand postiions!
5. Run python demo.py --gpu $GPU_ID
6. Boxes, meshes, MANO parameters, and renderings are saved at boxes, meshes, params, and renders, respectively.

The ${ROOT} is described as below.

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

• data contains data loading codes and soft links to images and annotations directories.
• demo contains the demo code
• common contains kernel code. You should put MANO_RIGHT.pkl and MANO_LEFT.pkl at common/utils/human_model_files/mano, where those are available in here.
• main contains high-level codes for training or testing the network.
• output contains log, trained models, visualized outputs, and test result.

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

${ROOT}
|-- data
|   |-- InterHand26M
|   |   |-- annotations
|   |   |   |-- train
|   |   |   |-- test
|   |   |-- images
|   |-- MSCOCO
|   |   |-- annotations
|   |   |   |-- coco_wholebody_train_v1.0.json
|   |   |   |-- coco_wholebody_val_v1.0.json
|   |   |   |-- MSCOCO_train_MANO_NeuralAnnot.json
|   |   |-- images
|   |   |   |-- train2017
|   |   |   |-- val2017
|   |-- HIC
|   |   |-- data
|   |   |   |-- HIC.json
|   |-- ReInterHand
|   |   |-- data
|   |   |   |-- m--*

• Download InterHand2.6M [HOMEPAGE]. images contains images in 5 fps, and annotations contains the H+M subset.
• Download the whole-body version of MSCOCO [HOMEPAGE]. MSCOCO_train_MANO_NeuralAnnot.json can be downloaded from [here].
• Download HIC [HOMEPAGE] [annotations]. You need to download 1) all Hand-Hand Interaction sequences (01.zip-14.zip) and 2) some of Hand-Object Interaction seuqneces (15.zip-21.zip) and 3) MANO fits. Or you can simply run python download.py in the data/HIC folder.
• Download ReInterHand[HOMEPAGE] at data/ReInterHand/data.

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.

• Prepare human_model_files folder following above Directory part and place it at common/utils/human_model_files.

In the main folder, run

python train.py --gpu 0-3

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.

• Checkpoint trained on IH26M (H+M) + MSCOCO. FYI, all experimental results of the paper is from a checkpoint trained on IH26M (H) + MSCOCO.
• Checkpoint trained on IH26M (H+M) + MSCOCO + ReInterHand (Mugsy_cameras).
• Checkpoint trained on IH26M (H+M) + MSCOCO + ReInterHand (Ego_cameras).
• Place the checkpoint at `output/model_dump'.
• Or if you want to test with our own trained model, place your model at output/model_dump.
• For the evaluation on InterHand2.6M dataset, we evaluated all methods in the paper on human_annot subset of interHand2.6M using data/InterHand26M/aid_human_annot_test.txt.

In the main folder, run

python test.py --gpu 0-3 --test_epoch 6

to test the network on the GPU 0,1,2,3 with snapshot_6.pth. --gpu 0,1,2,3 can be used instead of --gpu 0-3.

@inproceedings{moon2023interwild,  
author = {Moon, Gyeongsik},  
title = {Bringing Inputs to Shared Domains for {3D} Interacting Hands Recovery in the Wild},  
booktitle = {CVPR},  
year = {2023}  
} 

@inproceedings{moon2023reinterhand,
  title     = {A Dataset of Relighted {3D} Interacting Hands},
  author    = {Moon, Gyeongsik and Saito, Shunsuke and Xu, Weipeng and Joshi, Rohan and Buffalini, Julia and Bellan, Harley and Rosen, Nicholas and Richardson, Jesse and Mize Mallorie and Bree, Philippe and Simon, Tomas and Peng, Bo and Garg, Shubham and McPhail, Kevyn and Shiratori, Takaaki},
  booktitle = {NeurIPS Track on Datasets and Benchmarks},
  year      = {2023},
}

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

[Terms of Use] [Privacy Policy]