Author ORCID Identifier
https://orcid.org/ 0000-0002-1894-3360
Document Type
Conference Paper
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
Computer Sciences
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.
DOI
https://doi.org/10.21427/2k3e-0v06
Recommended Citation
Kunchala, A., Schoen-Phelan, B., Bouroche, M., & D'Arcy, L. (2022). SMPL-Based 3D Pedestrian Pose Prediction. Technological University Dublin. DOI: 10.21427/2K3E-0V06
Funder
Science Foundation Ireland
Publication Details
IEEE International Conference on Automatic Face and Gesture Recognition 2021