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

arXiv: https://arxiv.org/abs/2007.09590 · HTML: https://mlsanity.com/paper/2007.09590/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2020.

## Built on by (most cited first, 17 of 17)

- [Towards Accurate Alignment in Real-time 3D Hand-Mesh Reconstruction](https://mlsanity.com/paper/2109.01723/) (2021)
- [Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimation](https://mlsanity.com/paper/2111.08557/) (2021)
- [Efficient Annotation and Learning for 3D Hand Pose Estimation: A Survey](https://mlsanity.com/paper/2206.02257/) (2022)
- [Domain Adaptive Hand Keypoint and Pixel Localization in the Wild](https://mlsanity.com/paper/2203.08344/) (2022)
- [HandVoxNet++: 3D Hand Shape and Pose Estimation using Voxel-Based Neural Networks](https://mlsanity.com/paper/2107.01205/) (2021)
- [Decoupled Iterative Refinement Framework for Interacting Hands Reconstruction from a Single RGB Image](https://mlsanity.com/paper/2302.02410/) (2023)
- [TriHorn-Net: A Model for Accurate Depth-Based 3D Hand Pose Estimation](https://mlsanity.com/paper/2206.07117/) (2022)
- [Efficient Virtual View Selection for 3D Hand Pose Estimation](https://mlsanity.com/paper/2203.15458/) (2022)
- [Image-free Domain Generalization via CLIP for 3D Hand Pose Estimation](https://mlsanity.com/paper/2210.16788/) (2022)
- [DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single Camera](https://mlsanity.com/paper/2105.09880/) (2021)
- [Pyramid Deep Fusion Network for Two-Hand Reconstruction from RGB-D Images](https://mlsanity.com/paper/2307.06038/) (2023)
- [GeoHand: Unlocking Prior Geometry Knowledge for Monocular 3D Hand Reconstruction](https://mlsanity.com/paper/2605.17354/) (2026)
- [Exploiting Spatial-Temporal Context for Interacting Hand Reconstruction on Monocular RGB Video](https://mlsanity.com/paper/2308.04074/) (2023)
- [Depth Restoration of Hand-Held Transparent Objects for Human-to-Robot Handover](https://mlsanity.com/paper/2408.14997/) (2024)
- [EHPE: A Segmented Architecture for Enhanced Hand Pose Estimation](https://mlsanity.com/paper/2507.09560/) (2025)
- [UST-Hand: An Uncertainty-aware Spatiotemporal Point Cloud Interaction Network for 3D Self-supervised Hand Pose Estimation](https://mlsanity.com/paper/2605.17742/) (2026)
- [KAD-Net: Kinematics-Aware Decoupled Learning for Robust 3D Hand Pose Estimation from a Single Depth Image](https://mlsanity.com/paper/2609.12559/) (2026)

## Abstract (arXiv)

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.