AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation

Authors

  • Weiting Huang State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications
  • Pengfei Ren State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications
  • Jingyu Wang State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications
  • Qi Qi State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications
  • Haifeng Sun State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications

DOI:

https://doi.org/10.1609/aaai.v34i07.6761

Abstract

In this paper, we propose an adaptive weighting regression (AWR) method to leverage the advantages of both detection-based and regression-based method. 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 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.

Downloads

Published

2020-04-03

How to Cite

Huang, W., Ren, P., Wang, J., Qi, Q., & Sun, H. (2020). AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 11061-11068. https://doi.org/10.1609/aaai.v34i07.6761

Issue

Section

AAAI Technical Track: Vision