3DBODY.TECH 2017 - Paper 17.274

A. Saint et al., "Towards Automatic Human Body Model Fitting to a 3D Scan", in Proc. of 3DBODY.TECH 2017 - 8th Int. Conf. and Exh. on 3D Body Scanning and Processing Technologies, Montreal QC, Canada, 11-12 Oct. 2017, pp. 274-280, doi:10.15221/17.274.

Title:

Towards Automatic Human Body Model Fitting to a 3D Scan

Authors:

Alexandre SAINT 1, Abd El Rahman SHABAYEK 1,2, Djamila AOUADA 1, Björn OTTERSTEN 1, Kseniya CHERENKOVA 3, Gleb GUSEV 3

1 Interdisciplinary Centre for Security, Reliability, and Trust, University of Luxembourg, Luxembourg;
2 Computer Science department, Faculty of Computers and Informatics, Suez Canal University, Egypt;
3 Artec Europe S.a.r.l., Luxembourg, Luxembourg

Abstract:

This paper presents a method to automatically recover a realistic and accurate body shape of a person wearing clothing from a 3D scan. Indeed, in many practical situations, people are scanned wearing clothing. The underlying body shape is thus partially or completely occluded. Yet, it is very desirable to recover the shape of a covered body as it provides non-invasive means of measuring and analysing it. This is particularly convenient for patients in medical applications, customers in a retail shop, as well as in security applications where suspicious objects under clothing are to be detected. To recover the body shape from the 3D scan of a person in any pose, a human body model is usually fitted to the scan. Current methods rely on the manual placement of markers on the body to identify anatomical locations and guide the pose fitting. The markers are either physically placed on the body before scanning or placed in software as a postprocessing step. Some other methods detect key points on the scan using 3D feature descriptors to automate the placement of markers. They usually require a large database of 3D scans. We propose to automatically estimate the body pose of a person from a 3D mesh acquired by standard 3D body scanners, with or without texture. To fit a human model to the scan, we use joint locations as anchors. These are detected from multiple 2D views using a conventional body joint detector working on images. In contrast to existing approaches, the proposed method is fully automatic, and takes advantage of the robustness of state-of-art 2D joint detectors. The proposed approach is validated on scans of people in different poses wearing garments of various thicknesses and on scans of one person in multiple poses with known ground truth wearing close-fitting clothing.

Details:

Full paper: 17.274.pdf
Proceedings: 3DBODY.TECH 2017, 11-12 Oct. 2017, Montreal QC, Canada
Pages: 274-280
DOI: 10.15221/17.274

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