# Training a Feedback Loop for Hand Pose Estimation

arXiv: https://arxiv.org/abs/1609.09698 · HTML: https://mlsanity.com/paper/1609.09698/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2016.

## Built on by (most cited first, 21 of 21)

- [PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop](https://mlsanity.com/paper/2103.16507/) (2021)
- [MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare](https://mlsanity.com/paper/2212.06870/) (2022)
- [Embodied Hands: Modeling and Capturing Hands and Bodies Together](https://mlsanity.com/paper/2201.02610/) (2022)
- [Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object Interactions for Accurate 3D Pose Estimation](https://mlsanity.com/paper/2104.14639/) (2021)
- [FoundPose: Unseen Object Pose Estimation with Foundation Features](https://mlsanity.com/paper/2311.18809/) (2023)
- [Real-time Pose and Shape Reconstruction of Two Interacting Hands With a Single Depth Camera](https://mlsanity.com/paper/2106.08059/) (2021)
- [ReFit: Recurrent Fitting Network for 3D Human Recovery](https://mlsanity.com/paper/2308.11184/) (2023)
- [Hamba: Single-view 3D Hand Reconstruction with Graph-guided Bi-Scanning Mamba](https://mlsanity.com/paper/2407.09646/) (2024)
- [Single-view robot pose and joint angle estimation via render & compare](https://mlsanity.com/paper/2104.09359/) (2021)
- [RGB2Hands: Real-Time Tracking of 3D Hand Interactions from Monocular RGB Video](https://mlsanity.com/paper/2106.11725/) (2021)
- [BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image](https://mlsanity.com/paper/2105.09936/) (2021)
- [Efficient Annotation and Learning for 3D Hand Pose Estimation: A Survey](https://mlsanity.com/paper/2206.02257/) (2022)
- [Deep Feedback Inverse Problem Solver](https://mlsanity.com/paper/2101.07719/) (2021)
- [Diff-DOPE: Differentiable Deep Object Pose Estimation](https://mlsanity.com/paper/2310.00463/) (2023)
- [RealisticHands: A Hybrid Model for 3D Hand Reconstruction](https://mlsanity.com/paper/2108.13995/) (2021)
- [Learning Local Recurrent Models for Human Mesh Recovery](https://mlsanity.com/paper/2107.12847/) (2021)
- [Multi-view Image-based Hand Geometry Refinement using Differentiable Monte Carlo Ray Tracing](https://mlsanity.com/paper/2107.05509/) (2021)
- [Pyramid Deep Fusion Network for Two-Hand Reconstruction from RGB-D Images](https://mlsanity.com/paper/2307.06038/) (2023)
- [End-to-End 3D Hand Pose Estimation from Stereo Cameras](https://mlsanity.com/paper/2206.01384/) (2022)
- [AnthroNet: Conditional Generation of Humans via Anthropometrics](https://mlsanity.com/paper/2309.03812/) (2023)
- [A Training Method For VideoPose3D With Ideology of Action Recognition](https://mlsanity.com/paper/2206.06430/) (2022)

## Abstract (arXiv)

We propose an entirely data-driven approach to estimating the 3D pose of a hand given a depth image. We show that we can correct the mistakes made by a Convolutional Neural Network trained to predict an estimate of the 3D pose by using a feedback loop. The components of this feedback loop are also Deep Networks, optimized using training data. They remove the need for fitting a 3D model to the input data, which requires both a carefully designed fitting function and algorithm. We show that our approach outperforms state-of-the-art methods, and is efficient as our implementation runs at over 400 fps on a single GPU.