Awesome
<br /> <p align="center"> <p align="center"> <img src="docs/oakink_logo.png"" alt="Logo" width="30%"> </p> <h2 align="center">A Large-scale Knowledge Repository for Understanding Hand-Object Interaction </h2> <p align="center"> <a href="https://lixiny.github.io"><strong>Lixin Yang*</strong></a> · <a href="https://kailinli.top"><strong>Kailin Li*</strong></a> · <a href=""><strong>Xinyu Zhan*</strong></a> · <strong>Fei Wu</strong> · <a href="https://anran-xu.github.io"><strong>Anran Xu</strong></a> . <a href="https://liuliu66.github.io"><strong>Liu Liu</strong></a> · <a href="https://mvig.sjtu.edu.cn"><strong>Cewu Lu</strong></a> </p> <h3 align="center">CVPR 2022</h3> <div align="center"> <img src="docs/teaser.png" alt="Logo" width="100%"> </div> <br/> <p align="center"> <a href="https://arxiv.org/abs/2203.15709"> <img src='https://img.shields.io/badge/Paper-green?style=for-the-badge&logo=adobeacrobatreader&logoColor=white&labelColor=66cc00&color=94DD15' alt='Paper PDF'></a> <a href='https://oakink.net'> <img src='https://img.shields.io/badge/Project-orange?style=for-the-badge&logo=Google%20chrome&logoColor=white&labelColor=D35400' alt='Project Page'></a> <a href="https://www.youtube.com/watch?v=vNTdeXlLdU8"><img alt="youtube views" title="Subscribe to my YouTube channel" src="https://img.shields.io/badge/Video-red?style=for-the-badge&logo=youtube&labelColor=ce4630&logoColor=red"/></a> </p> </p>This repo contains the OakInk data toolkit (oikit) -- a Python package that provides data loading, splitting, and visualization tools for the OakInk knowledge repository.
OakInk contains three parts:
- OakBase: Object Affordance Knowledge (Oak) base, including objects' part-level segmentation and attributes.
- OakInk-Image: a video dataset with 3D hand-object pose and shape annotations.
- OakInk-Shape: a 3D grasping pose dataset with hand and object mesh models.
Summary on OakInk
- It contains 3D models, part segmentation, and affordance labels of 1,800 common household objects.
- It records human grasps with 100 (from 1,800) objects based on their affordances.
- It contains 792 multi-view video clips (230K images) complemented with annotation.
- Images are from four third-person views.
- It contains dynamic grasping and handover motions.
- It includes 3D ground-truth for MANO and objects.
- It contains a total of 50k hand-object interaction pose pairs involving the 1,800 objects.
- 1k are from the recording, 49K are done via interaction transfer.
Why use OakInk:
- For studying hand-object pose estimation and hand-held object reconstruction.
- For generating grasping pose, motion or handover with objects.
- For generating affordance-aware pose or motion for object manipulation.
- For transferring hand pose or motion to a new object.
Getting Started
Clone the repo
$ git clone https://github.com/lixiny/OakInk.git
- Install environment: see
docs/install.md
- Get datasets: see
docs/datasets.md
Load and Visualize
# visualize OakInk-Image mesh on sequence level:
# * --draw_mode [mesh, wireframe] to switch between mesh and wireframe
# * --seq_id: select sequence id from OAKINK_DIR/image/anno/seq_status.json to visualize
# * --view_id: select from [0, 1, 2, 3] for visualize from different views.
python scripts/viz_oakink_image_seq.py --draw_mode mesh --view_id 1
# use OakInkImage to load data_split: train, mode: subject (SP1) and visualize:
# * --data_split: select from [train, val, test, all]
# * --mode_split: select from [default, object, subject, handobject]
python scripts/viz_oakink_image.py --data_split train --mode_split subject
# use OakInkShape to load object category: teapot and intent: use:
# * --categories: select from OAKINK_DIR/shape/metaV2/yodaobject_cat.json, or "all"
# * --intent_mode: select from [use, hold, liftup, handover] or "all"
# * --data_split: select from [train, val, test, all]
python scripts/viz_oakink_shape.py --categories teapot --intent_mode use
# press `N` to load next sample
# use OakInkShape to load all the training grasps
python scripts/viz_oakink_shape.py --categories all --data_split train
# use OakInkShape to load all the training grasps in handover
python scripts/viz_oakink_shape.py --categories all --data_split train --intent_mode handover
Train and evaluate OakInk baselines
- Hand Mesh Recovery: recovery hand mesh from monocular image.
- Grasp Generation: generate human-like hand mesh grasping a given object.
Citation
If you find OakInk dataset and oikit useful for your research, please considering cite us:
@inproceedings{YangCVPR2022OakInk,
author = {Yang, Lixin and Li, Kailin and Zhan, Xinyu and Wu, Fei and Xu, Anran and Liu, Liu and Lu, Cewu},
title = {{OakInk}: A Large-Scale Knowledge Repository for Understanding Hand-Object Interaction},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}