# A Bayesian Framework for Sparse Representation-Based 3D Human Pose Estimation

arXiv: https://arxiv.org/abs/1412.0062 · HTML: https://mlsanity.com/paper/1412.0062/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2014.

## Built on by (most cited first, 3 of 3)

- [Covariance-Free Sparse Bayesian Learning](https://mlsanity.com/paper/2105.10439/) (2021)
- [High-Dimensional Sparse Bayesian Learning without Covariance Matrices](https://mlsanity.com/paper/2202.12808/) (2022)
- [Markerless Body Motion Capturing for 3D Character Animation based on Multi-view Cameras](https://mlsanity.com/paper/2212.05788/) (2022)

## Abstract (arXiv)

A Bayesian framework for 3D human pose estimation from monocular images based on sparse representation (SR) is introduced. Our probabilistic approach aims at simultaneously learning two overcomplete dictionaries (one for the visual input space and the other for the pose space) with a shared sparse representation. Existing SR-based pose estimation approaches only offer a point estimation of the dictionary and the sparse codes. Therefore, they might be unreliable when the number of training examples is small. Our Bayesian framework estimates a posterior distribution for the sparse codes and the dictionaries from labeled training data. Hence, it is robust to overfitting on small-size training data. Experimental results on various human activities show that the proposed method is superior to the state of-the-art pose estimation algorithms.