# Hand Pose Estimation: A Survey

arXiv: https://arxiv.org/abs/1903.01013 · HTML: https://mlsanity.com/paper/1903.01013/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2019.

## Built on by (most cited first, 8 of 8)

- [Effective Whole-body Pose Estimation with Two-stages Distillation](https://mlsanity.com/paper/2307.15880/) (2023)
- [Efficient Annotation and Learning for 3D Hand Pose Estimation: A Survey](https://mlsanity.com/paper/2206.02257/) (2022)
- [Video Action Differencing](https://mlsanity.com/paper/2503.07860/) (2025)
- [Multi-View Video-Based 3D Hand Pose Estimation](https://mlsanity.com/paper/2109.11747/) (2021)
- [At Your Fingertips: Extracting Piano Fingering Instructions from Videos](https://mlsanity.com/paper/2303.03745/) (2023)
- [A Review on Human Pose Estimation](https://mlsanity.com/paper/2110.06877/) (2021)
- [Learning Sequential Contexts using Transformer for 3D Hand Pose Estimation](https://mlsanity.com/paper/2206.00171/) (2022)
- [Mesh Represented Recycle Learning for 3D Hand Pose and Mesh Estimation](https://mlsanity.com/paper/2310.12189/) (2023)

## Abstract (arXiv)

The success of Deep Convolutional Neural Networks (CNNs) in recent years in almost all the Computer Vision tasks on one hand, and the popularity of low-cost consumer depth cameras on the other, has made Hand Pose Estimation a hot topic in computer vision field. In this report, we will first explain the hand pose estimation problem and will review major approaches solving this problem, especially the two different problems of using depth maps or RGB images. We will survey the most important papers in each field and will discuss the strengths and weaknesses of each. Finally, we will explain the biggest datasets in this field in detail and list 22 datasets with all their properties. To the best of our knowledge this is the most complete list of all the datasets in the hand pose estimation field.