Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/39834
Title: Generating personalized anatomy-based 3D facial models from scanned data
Authors: Zhang, Y.
Sim, T. 
Tan, C.L. 
Keywords: Anatomy-based model
Deformable model
Face reconstruction
Facial animation
Multi-layer skin/muscle/skull structure
Scanned data
Issue Date: 2005
Source: Zhang, Y.,Sim, T.,Tan, C.L. (2005). Generating personalized anatomy-based 3D facial models from scanned data. Machine Graphics and Vision 14 (1) : 3-28. ScholarBank@NUS Repository.
Abstract: This paper presents a new method for reconstructing animatable, anatomy-based human facial models from scanned range data. Our method adapts a prototype model that is suitable for physically-based animation to the geometry of a specific person's face with minimal user intervention. The prototype model has a known topology and incorporates a multi-layer structure of the skin, muscles, and skull. Based on a series of measurements between a subset of anthropometric landmarks specified on the prototype model and the scanned surface, an automated global alignment adapts the size, position, and orientation of the prototype model to align it with the scanned surface. In the skin layer adaptation, the generic skin mesh is represented as a dynamic deformable model which is subjected to internal force stemming from the elastic properties of the surface and external forces generated by the scanned data points and features. We automatically deform the underlying muscle layer consisting of three types of muscle models. A set of automatically generated skull feature points is then transformed based on the deformed external skin and muscle layers. The new positions of these feature points are used to drive volume morphing applied to the skull template for skull fitting. With the adapted multi-layer anatomical structure, the reconstructed model not only resembles the shape of the individual's face but can also be animated instantly using the muscle and jaw parameters.
Source Title: Machine Graphics and Vision
URI: http://scholarbank.nus.edu.sg/handle/10635/39834
ISSN: 12300535
Appears in Collections:Staff Publications

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