# DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation

arXiv: https://arxiv.org/abs/1708.08325 · HTML: https://mlsanity.com/paper/1708.08325/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2017.

## Built on by (most cited first, 19 of 19)

- [H2O: Two Hands Manipulating Objects for First Person Interaction Recognition](https://mlsanity.com/paper/2104.11181/) (2021)
- [Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-pixel Part Segmentation](https://mlsanity.com/paper/2107.00434/) (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)
- [HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud](https://mlsanity.com/paper/2404.03159/) (2024)
- [HandVoxNet++: 3D Hand Shape and Pose Estimation using Voxel-Based Neural Networks](https://mlsanity.com/paper/2107.01205/) (2021)
- [TriHorn-Net: A Model for Accurate Depth-Based 3D Hand Pose Estimation](https://mlsanity.com/paper/2206.07117/) (2022)
- [Modern Augmented Reality: Applications, Trends, and Future Directions](https://mlsanity.com/paper/2202.09450/) (2022)
- [A hybrid classification-regression approach for 3D hand pose estimation using graph convolutional networks](https://mlsanity.com/paper/2105.10902/) (2021)
- [FunHOI: Annotation-Free 3D Hand-Object Interaction Generation via Functional Text Guidance](https://mlsanity.com/paper/2502.20805/) (2025)
- [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)
- [HandDAGT: A Denoising Adaptive Graph Transformer for 3D Hand Pose Estimation](https://mlsanity.com/paper/2407.20542/) (2024)
- [Lightweight Estimation of Hand Mesh and Biomechanically Feasible Kinematic Parameters](https://mlsanity.com/paper/2303.14838/) (2023)
- [Self-supervised Optimization of Hand Pose Estimation using Anatomical Features and Iterative Learning](https://mlsanity.com/paper/2307.03007/) (2023)
- [DOR3D-Net: Dense Ordinal Regression Network for 3D Hand Pose Estimation](https://mlsanity.com/paper/2403.13405/) (2024)
- [Edge Weight Prediction For Category-Agnostic Pose Estimation](https://mlsanity.com/paper/2411.16665/) (2024)
- [HandMCM: Multi-modal Point Cloud-based Correspondence State Space Model for 3D Hand Pose Estimation](https://mlsanity.com/paper/2602.01586/) (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)

DeepPrior is a simple approach based on Deep Learning that predicts the joint 3D locations of a hand given a depth map. Since its publication early 2015, it has been outperformed by several impressive works. Here we show that with simple improvements: adding ResNet layers, data augmentation, and better initial hand localization, we achieve better or similar performance than more sophisticated recent methods on the three main benchmarks (NYU, ICVL, MSRA) while keeping the simplicity of the original method. Our new implementation is available at https://github.com/moberweger/deep-prior-pp .