Latest revision as of 19:35, 1 February 2021

Abstract

In this paper, a dataset, named 3DBodyTex, of static 3D body scans with high-quality texture information is presented along with a fully automatic method for body model fitting to a 3D scan. 3D shape modelling is a fundamental area of computer vision that has a wide range of applications in the industry. It is becoming even more important as 3D sensing technologies are entering consumer devices such as smartphones. As the main output of these sensors is the 3D shape, many methods rely on this information alone. The 3D shape information is, however, very high dimensional and leads to models that must handle many degrees of freedom from limited information. Coupling texture and 3D shape alleviates this burden, as the texture of 3D objects is complementary to their shape. Unfortunately, high-quality texture content is lacking from commonly available datasets, and in particular in datasets of 3D body scans. The proposed 3DBodyTex dataset aims to fill this gap with hundreds of high-quality 3D body scans with high-resolution texture. Moreover, a novel fully automatic pipeline to fit a body model to a 3D scan is proposed. It includes a robust 3D landmark estimator that takes advantage of the high-resolution texture of 3DBodyTex. The pipeline is applied to the scans, and the results are reported and discussed, showcasing the diversity of the features in the dataset.

Original document

The different versions of the original document can be found in:

• https://orbilu.uni.lu/bitstream/10993/36414/1/saint_et_al-3dbodytex-3dv_2018.pdf under the license https://creativecommons.org/licenses/by-nc-sa

• http://orbilu.uni.lu/handle/10993/36414,

https://cvdatasets.uni.lu under the license cc-by-nc-sa

• http://xplorestaging.ieee.org/ielx7/8490644/8490938/08491001.pdf?arnumber=8491001,

http://dx.doi.org/10.1109/3dv.2018.00063

• https://orbilu.uni.lu/handle/10993/36414,

https://orbilu.uni.lu/bitstream/10993/36414/1/saint_et_al-3dbodytex-3dv_2018.pdf,

https://dblp.uni-trier.de/db/conf/3dim/3dv2018.html#SaintASCGAO18,

https://academic.microsoft.com/#/detail/2898078621