Authors:
Gerald Adam Zwettler
1
;
2
;
Christoph Praschl
2
;
David Baumgartner
2
;
Tobias Zucali
3
;
Dora Turk
3
;
Martin Hanreich
2
and
Andreas Schuler
4
;
2
Affiliations:
1
Department of Software Engineering, School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria
;
2
Research Group Advanced Information Systems and Technology (AIST), University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria
;
3
AMB GmbH, amb-technology.ai, Hafenstraße 47-51 4020 Linz, Austria
;
4
Department of Bio and Medical Informatics, School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria
Keyword(s):
Elastic Shape Alignment, Human Body Pose Detection, 3D Body Reconstruction, Silhouette Reconstruction.
Abstract:
The 3D silhouette reconstruction of a human body rotating in front of a monocular camera system is a very challenging task due to elastic deformation and positional mismatch from body motion. Nevertheless, knowledge of the 3D body shape is a key information for precise determination of one’s clothing sizes, e.g. for precise shopping to reduce the number of return shipments in online retail. In this paper a novel three step alignment process is presented, utilizing As-Rigid-As-Possible (ARAP) transformations to normalize the body joint skeleton derived from OpenPose with a CGI rendered reference model in A- or T-pose. With further distance-map accelerated registration steps, positional mismatches and inaccuracies from the OpenPose joint estimation are compensated thus allowing for 3D silhouette reconstruction of a moving and elastic object without the need for sophisticated statistical shape models. Tests on both, artificial and real-world data, generally proof the practicability of t
his approach with all three alignment/registration steps essential and adequate for 3D silhouette reconstruction data normalization.
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