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- W4312121710 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." @default.
- W4312121710 created "2023-01-04" @default.
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- W4312121710 date "2022-12-21" @default.
- W4312121710 modified "2023-09-27" @default.
- W4312121710 title "Advanced Baseline for 3D Human Pose Estimation: A Two-Stage Approach" @default.
- W4312121710 doi "https://doi.org/10.48550/arxiv.2212.11344" @default.
- W4312121710 hasPublicationYear "2022" @default.
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