# MVHM: A Large-Scale Multi-View Hand Mesh Benchmark for Accurate 3D Hand Pose Estimation

arXiv: https://arxiv.org/abs/2012.03206 · HTML: https://mlsanity.com/paper/2012.03206/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2020.

## Built on by (most cited first, 15 of 15)

- [MobRecon: Mobile-Friendly Hand Mesh Reconstruction from Monocular Image](https://mlsanity.com/paper/2112.02753/) (2021)
- [TOCH: Spatio-Temporal Object-to-Hand Correspondence for Motion Refinement](https://mlsanity.com/paper/2205.07982/) (2022)
- [AlignSDF: Pose-Aligned Signed Distance Fields for Hand-Object Reconstruction](https://mlsanity.com/paper/2207.12909/) (2022)
- [UmeTrack: Unified multi-view end-to-end hand tracking for VR](https://mlsanity.com/paper/2211.00099/) (2022)
- [TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2110.09554/) (2021)
- [PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation](https://mlsanity.com/paper/2209.08194/) (2022)
- [Single-to-Dual-View Adaptation for Egocentric 3D Hand Pose Estimation](https://mlsanity.com/paper/2403.04381/) (2024)
- [Efficient Annotation and Learning for 3D Hand Pose Estimation: A Survey](https://mlsanity.com/paper/2206.02257/) (2022)
- [RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation](https://mlsanity.com/paper/2309.09301/) (2023)
- [Physics-aware Hand-object Interaction Denoising](https://mlsanity.com/paper/2405.11481/) (2024)
- [Multi-View Video-Based 3D Hand Pose Estimation](https://mlsanity.com/paper/2109.11747/) (2021)
- [RUMPL: Ray-Based Transformers for Universal Multi-View 2D to 3D Human Pose Lifting](https://mlsanity.com/paper/2512.15488/) (2025)
- [Learning Sequential Contexts using Transformer for 3D Hand Pose Estimation](https://mlsanity.com/paper/2206.00171/) (2022)
- [REACH: Hand Pose Estimation from Room Corners](https://mlsanity.com/paper/2605.22231/) (2026)
- [VEPHand: View-Efficient Photometric Hand Performance Capture at Scale](https://mlsanity.com/paper/2606.15966/) (2026)

## Abstract (arXiv)

Estimating 3D hand poses from a single RGB image is challenging because depth ambiguity leads the problem ill-posed. Training hand pose estimators with 3D hand mesh annotations and multi-view images often results in significant performance gains. However, existing multi-view datasets are relatively small with hand joints annotated by off-the-shelf trackers or automated through model predictions, both of which may be inaccurate and can introduce biases. Collecting a large-scale multi-view 3D hand pose images with accurate mesh and joint annotations is valuable but strenuous. In this paper, we design a spin match algorithm that enables a rigid mesh model matching with any target mesh ground truth. Based on the match algorithm, we propose an efficient pipeline to generate a large-scale multi-view hand mesh (MVHM) dataset with accurate 3D hand mesh and joint labels. We further present a multi-view hand pose estimation approach to verify that training a hand pose estimator with our generated dataset greatly enhances the performance. Experimental results show that our approach achieves the performance of 0.990 in AUC20-50 on the MHP dataset compared to the previous state-of-the-art of 0.939 on this dataset. Our datasset is public available. footnoteurlhttps://github.com/Kuzphi/MVHM Our datasset is available at~hrefhttps://github.com/Kuzphi/MVHMcolorbluehttps://github.com/Kuzphi/MVHM.