AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation

PubDate: Jul 2020

Teams: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, 2EBUPT Information Technology Co., Ltd.

Writers: Weiting Huang, Pengfei Ren, Jingyu Wang, Qi Qi, Haifeng Sun

PDF: AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation

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Abstract

In this paper, we propose an adaptive weighting regression (AWR) method to leverage the advantages of both detection-based and regression-based methods. Hand joint coordinates are estimated as discrete integration of all pixels in dense representation, guided by adaptive weight maps. This learnable aggregation process introduces both dense and joint supervision that allows end-to-end training and brings adaptability to weight maps, making the network more accurate and robust. Comprehensive exploration experiments are conducted to validate the effectiveness and generality of AWR under various experimental settings, especially its usefulness for different types of dense representation and input modality. Our method outperforms other state-of-the-art methods on four publicly available datasets, including NYU, ICVL, MSRA and HANDS 2017 dataset.