Work

Deep NRSFM for multi-view multi-body pose estimation

Year: 2024

Type: article

Abstract: This paper addresses the challenging task of unsupervised relative human pose estimation. Our solution exploits the potential offered by utilizing multiple uncalibrated cameras. It is assumed that spatial human pose and camera parameter estimation can be solved as a block sparse dictionary learning problem with zero supervision. The resulting structures and camera parameters can fit individual skeletons into a common space. To do so, we exploit the fact that all individuals in the image are viewed from the same camera viewpoint, thus exploiting the information provided by multiple camera views and overcoming the lack of information on camera parameters. To the best of our knowledge, this is the first solution that requires neither 3D ground truth nor knowledge of the intrinsic or extrinsic camera parameters. Our approach demonstrates the potential of using multiple viewpoints to solve challenging computer vision problems. Additionally, we provide access to the code, encouraging further development and experimentation. https://github.com/Jeryoss/MVMB-NRSFM.

Source: Pattern Recognition Letters

DOI: 10.1016/j.patrec.2024.08.015

Authors: Áron Fóthi, Joul Skaf, Fengjiao Lu, Kristian Fenech

Institution: Eötvös Loránd University

Language: English

Topic: Human Pose and Action Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,610

Funders: European Commission, Department of Artificial Intelligence, Korea University