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

• URL: http://arxiv.org/abs/2012.03206v1
• Date: Sun, 6 Dec 2020 07:55:08 GMT
• Title: MVHM: A Large-Scale Multi-View Hand Mesh Benchmark for Accurate 3D Hand Pose Estimation
• Authors: Liangjian Chen, Shih-Yao Lin, Yusheng Xie, Yen-Yu Lin, and Xiaohui Xie
• Abstract summary: Estimating 3D hand poses from a single RGB image is challenging because depth ambiguity leads the problem ill-posed. We design a spin match algorithm that enables a rigid mesh model matching with any target mesh ground truth. We present a multi-view hand pose estimation approach to verify that training a hand pose estimator with our generated dataset greatly enhances the performance.
• Score: 32.12879364117658
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 $\text{AUC}_{\text{20-50}}$ on the MHP dataset compared to the previous state-of-the-art of 0.939 on this dataset. Our datasset is public available. \footnote{\url{https://github.com/Kuzphi/MVHM}} Our datasset is available at~\href{https://github.com/Kuzphi/MVHM}{\color{blue}{https://github.com/Kuzphi/MVHM}}.

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