Advanced Baseline for 3D Human Pose Estimation: A Two-Stage Approach
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Abstract (arXiv)
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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References · 9
- 2017A simple yet effective baseline for 3d human pose estimationcited by 401
- 2016Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Posecited by 220
- 2020Deep Learning-Based Human Pose Estimation: A Surveycited by 205
- 20163D Human Pose Estimation = 2D Pose Estimation + Matchingcited by 112
- 2019RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimationcited by 85
- 2021Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flowscited by 84
- 2020Monocular Human Pose Estimation: A Survey of Deep Learning-based Methodscited by 59
- 2017Human Pose Regression by Combining Indirect Part Detection and Contextual Informationcited by 52
- 2020HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimationcited by 44
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