# Hands Deep in Deep Learning for Hand Pose Estimation

arXiv: https://arxiv.org/abs/1502.06807 · HTML: https://mlsanity.com/paper/1502.06807/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2015.

## Built on by (most cited first, 20 of 20)

- [Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-pixel Part Segmentation](https://mlsanity.com/paper/2107.00434/) (2021)
- [Multi-Anchor Active Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2108.08012/) (2021)
- [Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild](https://mlsanity.com/paper/2209.12009/) (2022)
- [HandFoldingNet: A 3D Hand Pose Estimation Network Using Multiscale-Feature Guided Folding of a 2D Hand Skeleton](https://mlsanity.com/paper/2108.05545/) (2021)
- [Attention guided global enhancement and local refinement network for semantic segmentation](https://mlsanity.com/paper/2204.04363/) (2022)
- [TriHorn-Net: A Model for Accurate Depth-Based 3D Hand Pose Estimation](https://mlsanity.com/paper/2206.07117/) (2022)
- [Boundary Guided Context Aggregation for Semantic Segmentation](https://mlsanity.com/paper/2110.14587/) (2021)
- [A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets](https://mlsanity.com/paper/2204.03328/) (2022)
- [MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation](https://mlsanity.com/paper/2301.07354/) (2023)
- [Generalized Product-of-Experts for Learning Multimodal Representations in Noisy Environments](https://mlsanity.com/paper/2211.03587/) (2022)
- [Single-View Scene Point Cloud Human Grasp Generation](https://mlsanity.com/paper/2404.15815/) (2024)
- [Modern Augmented Reality: Applications, Trends, and Future Directions](https://mlsanity.com/paper/2202.09450/) (2022)
- [Analysis of the performance of U-Net neural networks for the segmentation of living cells](https://mlsanity.com/paper/2210.01538/) (2022)
- [GeoHand: Unlocking Prior Geometry Knowledge for Monocular 3D Hand Reconstruction](https://mlsanity.com/paper/2605.17354/) (2026)
- [Cardiac fat segmentation using computed tomography and an image-to-image conditional generative adversarial neural network](https://mlsanity.com/paper/2605.20064/) (2026)
- [HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation](https://mlsanity.com/paper/2106.04269/) (2021)
- [Pushing the Envelope for Depth-Based Semi-Supervised 3D Hand Pose Estimation with Consistency Training](https://mlsanity.com/paper/2303.15147/) (2023)
- [JetSeg: Efficient Real-Time Semantic Segmentation Model for Low-Power GPU-Embedded Systems](https://mlsanity.com/paper/2305.11419/) (2023)
- [Augmented Efficiency: Reducing Memory Footprint and Accelerating Inference for 3D Semantic Segmentation through Hybrid Vision](https://mlsanity.com/paper/2407.16102/) (2024)
- [Touchless Intraoperative Image Access System Based on Vision-Based Hand Tracking](https://mlsanity.com/paper/2604.24235/) (2026)

## Abstract (arXiv)

We introduce and evaluate several architectures for Convolutional Neural Networks to predict the 3D joint locations of a hand given a depth map. We first show that a prior on the 3D pose can be easily introduced and significantly improves the accuracy and reliability of the predictions. We also show how to use context efficiently to deal with ambiguities between fingers. These two contributions allow us to significantly outperform the state-of-the-art on several challenging benchmarks, both in terms of accuracy and computation times.