# Rotation-invariant Mixed Graphical Model Network for 2D Hand Pose Estimation

arXiv: https://arxiv.org/abs/2002.02033 · HTML: https://mlsanity.com/paper/2002.02033/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2110.09554/) (2021)
- [PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation](https://mlsanity.com/paper/2209.08194/) (2022)
- [Identity-Aware Hand Mesh Estimation and Personalization from RGB Images](https://mlsanity.com/paper/2209.10840/) (2022)
- [ARShoe: Real-Time Augmented Reality Shoe Try-on System on Smartphones](https://mlsanity.com/paper/2108.10515/) (2021)
- [Fast Monocular Hand Pose Estimation on Embedded Systems](https://mlsanity.com/paper/2102.07067/) (2021)
- [Distilling Token-Pruned Pose Transformer for 2D Human Pose Estimation](https://mlsanity.com/paper/2304.05548/) (2023)

## Abstract (arXiv)

In this paper, we propose a new architecture named Rotation-invariant Mixed Graphical Model Network (R-MGMN) to solve the problem of 2D hand pose estimation from a monocular RGB image. By integrating a rotation net, the R-MGMN is invariant to rotations of the hand in the image. It also has a pool of graphical models, from which a combination of graphical models could be selected, conditioning on the input image. Belief propagation is performed on each graphical model separately, generating a set of marginal distributions, which are taken as the confidence maps of hand keypoint positions. Final confidence maps are obtained by aggregating these confidence maps together. We evaluate the R-MGMN on two public hand pose datasets. Experiment results show our model outperforms the state-of-the-art algorithm which is widely used in 2D hand pose estimation by a noticeable margin.