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arXiv · 2105.10904

Skeleton-aware multi-scale heatmap regression for 2D hand pose estimation

Ikram Kourbane·Yakup Genc
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Abstract

Existing RGB-based 2D hand pose estimation methods learn the joint locations from a single resolution, which is not suitable for different hand sizes. To tackle this problem, we propose a new deep learning-based framework that consists of two main modules. The former presents a segmentation-based approach to detect the hand skeleton and localize the hand bounding box. The second module regresses the 2D joint locations through a multi-scale heatmap regression approach that exploits the predicted hand skeleton as a constraint to guide the model. Furthermore, we construct a new dataset that is suitable for both hand detection and pose estimation. We qualitatively and quantitatively validate our method on two datasets. Results demonstrate that the proposed method outperforms state-of-the-art and can recover the pose even in cluttered images and complex poses.

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Ikram Kourbane, Yakup Genc. 2021-05-23. Skeleton-aware multi-scale heatmap regression for 2D hand pose estimation. https://arxiv.org/abs/2105.10904

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