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On the Role of Depth Predictions for 3D Human Pose Estimation

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Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1 (FTC 2022 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 559))

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Abstract

Following the successful application of deep convolutional neural networks to 2D human pose estimation, the next logical problem to solve is static 3D human pose estimation from monocular images. While previous solutions have shown some success, they do not fully utilize the depth information from the 2D inputs. With the goal of addressing this depth ambiguity, we build a system that takes 2D joint locations as input along with their estimated depth value and predicts their 3D positions in camera coordinates. Our system out performs comparable frame-by-frame 3D human pose estimation networks on the largest publicly available 3d motion data set, Human 3.6M. To provide further evidence for the usefulness of predicted depth values in the 3D pose estimation problem, we perform an extensive statistical analysis showing that even with potentially noisy depth predictions there is still a statistically significant correlation between the predicted depth value and the true depth value.

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Authors and Affiliations

  1. Inseer Inc., Charlottesville, Virginia, USA

    Alec Diaz-Arias, Dmitriy Shin, Mitchell Messmore & Stephen Baek

  2. University of Virginia, Charlottesville, Virginia, USA

    Stephen Baek

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  1. Alec Diaz-Arias
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  2. Dmitriy Shin
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  3. Mitchell Messmore
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  4. Stephen Baek
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Correspondence to Alec Diaz-Arias .

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  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Diaz-Arias, A., Shin, D., Messmore, M., Baek, S. (2023). On the Role of Depth Predictions for 3D Human Pose Estimation. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-18461-1_15

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