# 2D-3D Pose Consistency-based Conditional Random Fields for 3D Human Pose Estimation

## TL;DR

This paper introduces a CRF-based model that leverages 2D pose heatmaps and a 3D pose prior to improve 3D human pose estimation from a single RGB image, achieving state-of-the-art results.

## Contribution

It proposes a novel CRF framework integrating 2D heatmap regression and 3D pose consistency, with an N-best inference strategy for improved accuracy.

## Key findings

- Achieves state-of-the-art performance on Human3.6M and HumanEva datasets.
- Effectively integrates 2D and 3D pose information through CRF modeling.
- Demonstrates the effectiveness of the proposed inference algorithm.

## Abstract

This study considers the 3D human pose estimation problem in a single RGB image by proposing a conditional random field (CRF) model over 2D poses, in which the 3D pose is obtained as a byproduct of the inference process. The unary term of the proposed CRF model is defined based on a powerful heat-map regression network, which has been proposed for 2D human pose estimation. This study also presents a regression network for lifting the 2D pose to 3D pose and proposes the prior term based on the consistency between the estimated 3D pose and the 2D pose. To obtain the approximate solution of the proposed CRF model, the N-best strategy is adopted. The proposed inference algorithm can be viewed as sequential processes of bottom-up generation of 2D and 3D pose proposals from the input 2D image based on deep networks and top-down verification of such proposals by checking their consistencies. To evaluate the proposed method, we use two large-scale datasets: Human3.6M and HumanEva. Experimental results show that the proposed method achieves the state-of-the-art 3D human pose estimation performance.