SMPL-Based 3D Pedestrian Pose Prediction
Citation:
A. Kunchala, M. Bouroche, L. D'Arcy and B. Schoen-Phelan, "SMPL-Based 3D Pedestrian Pose Prediction," 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), 2021, pp. 1-8Download Item:
Abstract:
Modeling human motion is a long-standing problem in computer vision. The rapid development of deep learning technologies for computer vision problems resulted in increased attention in the area of pose prediction due to its vital role in a multitude of applications, for example, behavior analysis, autonomous vehicles, and visual surveillance. In 3D pedestrian pose prediction, joint-rotation-based pose representation is extensively used due to the unconstrained degree of freedom for each joint and its ability to regress the 3D statistical wireframe. However, all the existing joint-rotation-based pose prediction approaches ignore the centrality of the distinct pose parameter components and are consequently prone to suffer from error accumulation along the kinematic chain, which results in unnatural human poses. In joint-rotation-based pose prediction, Skinned Multi-Person Linear (SMPL) parameters are widely used to represent pedestrian pose. In this work, a novel SMPL-based pose prediction network is proposed to address the centrality of each SMPL component by distributing the network weights among them. Furthermore, to constrain the network to generate only plausible human poses, an adversarial training approach is employed. The effectiveness of the proposed network is evaluated using the PedX and BEHAVE datasets. The proposed approach significantly outperforms state-of-the-art methods with improved prediction accuracy and generates plausible human pose predictions
Sponsor
Grant Number
Science Foundation Ireland (SFI)
18/CRT/6224
Author's Homepage:
http://people.tcd.ie/bourocmDescription:
PUBLISHED
Author: Bouroche, Melanie
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16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)Type of material:
Conference PaperCollections
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Full text availableDOI:
http://dx.doi.org/10.1109/fg52635.2021.9667016Metadata
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