3D face morphable models "In-The-Wild"

Booth, James and Antonakos, Epameinondas and Ploumpis, Stylianos and Trigeorgis, George and Panagakis, Yannis and Zafeiriou, Stefanos (2017) 3D face morphable models "In-The-Wild". In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 21-26 Jul 2017, Honolulu, HI, USA.

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Abstract

3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and among the state-of-the-art methods for reconstructing facial shape from single images. With the advent of new 3D sensors, many 3D facial datasets have been collected containing both neutral as well as expressive faces. However, all datasets are captured under controlled conditions. Thus, even though powerful 3D facial shape models can be learnt from such data, it is difficult to build statistical texture models that are sufficient to reconstruct faces captured in unconstrained conditions (in-the-wild). In this paper, we propose the first, to the best of our knowledge, in-the-wild 3DMM by combining a powerful statistical model of facial shape, which describes both identity and expression, with an in-the-wild texture model. We show that the employment of such an in-the-wild texture model greatly simplifies the fitting procedure, because there is no need to optimise with regards to the illumination parameters. Furthermore, we propose a new fast algorithm for fitting the 3DMM in arbitrary images. Finally, we have captured the first 3D facial database with relatively unconstrained conditions and report quantitative evaluations with state-of-the-art performance. Complementary qualitative reconstruction results are demonstrated on standard in-the-wild facial databases.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology
Item ID: 23786
Notes on copyright: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
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Depositing User: Yannis Panagakis
Date Deposited: 07 Mar 2018 17:01
Last Modified: 10 Sep 2018 16:17
URI: http://eprints.mdx.ac.uk/id/eprint/23786

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