Login | Register

Parametric design for human body modeling by wireframe-assisted deep learning


Parametric design for human body modeling by wireframe-assisted deep learning

Huang, Jida, Kwok, Tsz-Ho ORCID: https://orcid.org/0000-0001-7240-1426 and Zhou, Chi (2019) Parametric design for human body modeling by wireframe-assisted deep learning. Computer-Aided Design, 108 . pp. 19-29. ISSN 00104485

[thumbnail of CAD18_WireDeep.pdf]
Text (application/pdf)
CAD18_WireDeep.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Official URL: http://dx.doi.org/10.1016/j.cad.2018.10.004


Statistical learning of human body shape can be used for reconstructing or estimating body shapes from incomplete data, semantic parametric design, modifying images and videos, or simulation. A digital human body is normally represented in a high-dimensional space, and the number of vertices in a mesh is far larger than the number of human bodies in public available databases, which results in a model learned by Principle Component Analysis (PCA) can hardly reflect the true variety in human body shapes. While deep learning has been most successful on data with an underlying Euclidean or grid-like structure, the geometric nature of human body is non-Euclidean, it will be very challenging to perform deep learning techniques directly on such non-Euclidean domain. This paper presents a deep neural network (DNN) based hierarchical method for statistical learning of human body by using feature wireframe as one of the layers to separate the whole problem into smaller and more solvable sub-problems. The feature wireframe is a collection of feature curves which are semantically defined on the mesh of human body, and it is consistent to all human bodies. A set of patches can then be generated by clustering the whole mesh surface to separated ones that interpolate the feature wireframe. Since the surface is separated into patches, PCA only needs to be conducted on each patch but not on the whole surface. The spatial relationships between the semantic parameter, the wireframe and the patches are learned by DNN and linear regression respectively. An application of semantic parametric design is used to demonstrate the capability of the method, where the semantic parameters are linked to the feature wireframe instead of the mesh directly. Under this hierarchy, the feature wireframe acts like an agent between semantic parameters and the mesh, and also contains semantic meaning by itself. The proposed method of learning human body statistically with the help of feature wireframe is scalable and has a better quality.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering
Item Type:Article
Authors:Huang, Jida and Kwok, Tsz-Ho and Zhou, Chi
Journal or Publication:Computer-Aided Design
  • Natural Sciences & Engineering Research Council of Canada (NSERC)
  • National Science Foundation (NSF)
Digital Object Identifier (DOI):10.1016/j.cad.2018.10.004
Keywords:Feature, Wireframe, Human body, Deep learning, Parametric design
ID Code:985963
Deposited By: Tsz Ho Kwok
Deposited On:11 Feb 2020 17:01
Last Modified:28 Oct 2020 01:00
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top