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

• URL: http://arxiv.org/abs/2212.11344v1
• Date: Wed, 21 Dec 2022 20:31:39 GMT
• Title: Advanced Baseline for 3D Human Pose Estimation: A Two-Stage Approach
• Authors: Zichen Gui, Jungang Luo
• Abstract summary: This paper focuses on the 2D-to-3D lifting process in the two-stage methods and proposed a more advanced baseline model for 3D human pose estimation. Our improvements include optimization of machine learning models and multiple parameters, as well as introduction of a weighted loss to the training model.
• Score: 1.52292571922932
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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