# BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis

arXiv: https://arxiv.org/abs/1704.02612 · HTML: https://mlsanity.com/paper/1704.02612/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2017.

## Built on by (most cited first, 30 of 46)

- [HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction](https://mlsanity.com/paper/2203.01577/) (2022)
- [DexYCB: A Benchmark for Capturing Hand Grasping of Objects](https://mlsanity.com/paper/2104.04631/) (2021)
- [ContactOpt: Optimizing Contact to Improve Grasps](https://mlsanity.com/paper/2104.07267/) (2021)
- [OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction](https://mlsanity.com/paper/2203.15709/) (2022)
- [An Outlook into the Future of Egocentric Vision](https://mlsanity.com/paper/2308.07123/) (2023)
- [ParaHome: Parameterizing Everyday Home Activities Towards 3D Generative Modeling of Human-Object Interactions](https://mlsanity.com/paper/2401.10232/) (2024)
- [Collaborative Learning for Hand and Object Reconstruction with Attention-guided Graph Convolution](https://mlsanity.com/paper/2204.13062/) (2022)
- [AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand Pose](https://mlsanity.com/paper/2309.08942/) (2023)
- [A Dataset of Relighted 3D Interacting Hands](https://mlsanity.com/paper/2310.17768/) (2023)
- [GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities](https://mlsanity.com/paper/2412.04244/) (2024)
- [ZoomNAS: Searching for Whole-body Human Pose Estimation in the Wild](https://mlsanity.com/paper/2208.11547/) (2022)
- [emg2pose: A Large and Diverse Benchmark for Surface Electromyographic Hand Pose Estimation](https://mlsanity.com/paper/2412.02725/) (2024)
- [EgoPressure: A Dataset for Hand Pressure and Pose Estimation in Egocentric Vision](https://mlsanity.com/paper/2409.02224/) (2024)
- [HUMBI: A Large Multiview Dataset of Human Body Expressions and Benchmark Challenge](https://mlsanity.com/paper/2110.00119/) (2021)
- [Efficient Annotation and Learning for 3D Hand Pose Estimation: A Survey](https://mlsanity.com/paper/2206.02257/) (2022)
- [Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild](https://mlsanity.com/paper/2209.12009/) (2022)
- [EvHandPose: Event-based 3D Hand Pose Estimation with Sparse Supervision](https://mlsanity.com/paper/2303.02862/) (2023)
- [3D Pose Estimation of Two Interacting Hands from a Monocular Event Camera](https://mlsanity.com/paper/2312.14157/) (2023)
- [H2O: A Benchmark for Visual Human-human Object Handover Analysis](https://mlsanity.com/paper/2104.11466/) (2021)
- [A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/2304.03635/) (2023)
- [Dense Hand-Object(HO) GraspNet with Full Grasping Taxonomy and Dynamics](https://mlsanity.com/paper/2409.04033/) (2024)
- [Domain Adaptive Hand Keypoint and Pixel Localization in the Wild](https://mlsanity.com/paper/2203.08344/) (2022)
- [HandVoxNet++: 3D Hand Shape and Pose Estimation using Voxel-Based Neural Networks](https://mlsanity.com/paper/2107.01205/) (2021)
- [A Survey on RGB-D Datasets](https://mlsanity.com/paper/2201.05761/) (2022)
- [DyTact: Capturing Dynamic Contacts in Hand-Object Manipulation](https://mlsanity.com/paper/2506.03103/) (2025)
- [Adversarial Motion Modelling helps Semi-supervised Hand Pose Estimation](https://mlsanity.com/paper/2106.05954/) (2021)
- [Multi-View Video-Based 3D Hand Pose Estimation](https://mlsanity.com/paper/2109.11747/) (2021)
- [Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimation](https://mlsanity.com/paper/2112.06389/) (2021)
- [3D Hand Pose and Shape Estimation from RGB Images for Keypoint-Based Hand Gesture Recognition](https://mlsanity.com/paper/2109.13879/) (2021)
- [A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets](https://mlsanity.com/paper/2204.03328/) (2022)

## Abstract (arXiv)

In this paper we introduce a large-scale hand pose dataset, collected using a novel capture method. Existing datasets are either generated synthetically or captured using depth sensors: synthetic datasets exhibit a certain level of appearance difference from real depth images, and real datasets are limited in quantity and coverage, mainly due to the difficulty to annotate them. We propose a tracking system with six 6D magnetic sensors and inverse kinematics to automatically obtain 21-joints hand pose annotations of depth maps captured with minimal restriction on the range of motion. The capture protocol aims to fully cover the natural hand pose space. As shown in embedding plots, the new dataset exhibits a significantly wider and denser range of hand poses compared to existing benchmarks. Current state-of-the-art methods are evaluated on the dataset, and we demonstrate significant improvements in cross-benchmark performance. We also show significant improvements in egocentric hand pose estimation with a CNN trained on the new dataset.