Advanced Baseline for 3D Human Pose Estimation: A Two-Stage Approach

TL;DR

This paper introduces an improved two-stage baseline model for 3D human pose estimation, enhancing existing methods through optimized models and weighted loss, validated on the Human3.6M benchmark.

Contribution

The paper presents a more advanced baseline for 3D human pose estimation focusing on the 2D-to-3D lifting process with optimized models and a weighted loss function.

Findings

Achieved satisfactory results on Human3.6M benchmark.

Enhanced 2D-to-3D lifting with weighted loss.

Improved model optimization techniques.

Abstract

Human pose estimation has been widely applied in various industries. While recent decades have witnessed the introduction of many advanced two-dimensional (2D) human pose estimation solutions, three-dimensional (3D) human pose estimation is still an active research field in computer vision. Generally speaking, 3D human pose estimation methods can be divided into two categories: single-stage and two-stage. In this paper, we focused on the 2D-to-3D lifting process in the two-stage methods and proposed a more advanced baseline model for 3D human pose estimation, based on the existing solutions. Our improvements include optimization of machine learning models and multiple parameters, as well as introduction of a weighted loss to the training model. Finally, we used the Human3.6M benchmark to test the final performance and it did produce satisfactory results.