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The space of human body shapes: reconstruction and parameterization from range scans
Abstract
We develop a novel method for fitting high-resolution template meshes to detailed human body range scans with sparse 3D markers. We formulate an optimization problem in which the degrees of freedom are an affine transformation at each template vertex. The objective function is a weighted combination of three measures: proximity of transformed vertices to the range data, similarity between neighboring transformations, and proximity of sparse markers at corresponding locations on the template and target surface. We solve for the transformations with a non-linear optimizer, run at two resolutions to speed convergence. We demonstrate reconstruction and consistent parameterization of 250 human body models. With this parameterized set, we explore a variety of applications for human body modeling, including: morphing, texture transfer, statistical analysis of shape, model fitting from sparse markers, feature analysis to modify multiple correlated parameters (such as the weight and height of an individual), and transfer of surface detail and animation controls from a template to fitted models.
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“After a few steps, more emphasis is put on the data term because the original shape has been deformed to lie close enough to the aligning shape [29], [30].”
From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [29]
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MethodZippered polygon meshes from range images
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Lee, W., Lee, B., Yang, X., Jung, H., Bok, I., Kim, C., Kwon, O., & You, H. (2018) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.01.023 · Cited by 48
SoftSMPL: Data‐driven Modeling of Nonlinear Soft‐tissue Dynamics for Parametric Humans
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Randomized cuts for 3D mesh analysis
Golovinskiy, A., & Funkhouser, T. A. (2008) · ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) · doi:10.1145/1457515.1409098 · Cited by 48
Metadata
- Authors
- Allen, B., Curless, B., & Popović, Z.
- Affiliations
- University of Washington (US)
- Venue
- ACM SIGGRAPH 2003 Papers
- Publisher
- ACM
- Published
- 2003-07
- Pages
- 587-594
- OpenAlex
- W2099011563
- Cited by
- 913
- References
- 23
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2 · 2023: 5 · 2022: 9 · 2021: 36 · 2020: 58 · 2019: 44 · 2018: 73 · 2017: 58 · 2016: 48 · 2015: 57 · 2014: 71 · 2013: 60 · 2012: 45
- FWCI
- 60.72
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGGRAPH Conference (SIGGRAPH) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- human body modeling, range scans, affine transformation, texture transfer, statistical shape analysis
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