DIREG3D: DIrectly REGress 3D Hands from Multiple Cameras

• URL: http://arxiv.org/abs/2201.11187v1
• Date: Wed, 26 Jan 2022 21:03:56 GMT
• Title: DIREG3D: DIrectly REGress 3D Hands from Multiple Cameras
• Authors: Ashar Ali, Upal Mahbub, Gokce Dane, Gerhard Reitmayr
• Abstract summary: DIREG3D is capable of utilizing camera parameters, 3D geometry, intermediate 2D cues, and visual information to regress parameters for accurately representing a Hand Mesh model. We extend these results to a multi-view camera setup by fusing features from different viewpoints.
• Score: 0.22940141855172028
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present DIREG3D, a holistic framework for 3D Hand Tracking. The proposed framework is capable of utilizing camera intrinsic parameters, 3D geometry, intermediate 2D cues, and visual information to regress parameters for accurately representing a Hand Mesh model. Our experiments show that information like the size of the 2D hand, its distance from the optical center, and radial distortion is useful for deriving highly reliable 3D poses in camera space from just monocular information. Furthermore, we extend these results to a multi-view camera setup by fusing features from different viewpoints.

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