Related papers: Learning Anthropometry from Rendered Humans

Learning Anthropometry from Rendered Humans

URL: http://arxiv.org/abs/2101.02515v1
Date: Thu, 7 Jan 2021 12:26:39 GMT
Title: Learning Anthropometry from Rendered Humans
Authors: Song Yan and Joni-Kristian K\"am\"ar\"ainen
Abstract summary: We introduce a new 3D scan dataset of 2,675 female and 1,474 male scans. We also introduce a small dataset of 200 RGB images and tape measured ground truth. With the help of the two new datasets we propose a part-based shape model and a deep neural network for estimating anthropometric measurements from 2D images.
Score: 6.939794498223168
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Accurate estimation of anthropometric body measurements from RGB images has many potential applications in industrial design, online clothing, medical diagnosis and ergonomics. Research on this topic is limited by the fact that there exist only generated datasets which are based on fitting a 3D body mesh to 3D body scans in the commercial CAESAR dataset. For 2D only silhouettes are generated. To circumvent the data bottleneck, we introduce a new 3D scan dataset of 2,675 female and 1,474 male scans. We also introduce a small dataset of 200 RGB images and tape measured ground truth. With the help of the two new datasets we propose a part-based shape model and a deep neural network for estimating anthropometric measurements from 2D images. All data will be made publicly available.

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