'''3D Face Morphable Models ''''In-the-Wild'''''''
| Autor: | Stefanos Zafeiriou, Yannis Panagakis, James Booth, George Trigeorgis, Stylianos Ploumpis, Epameinondas Antonakos |
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| Přispěvatelé: | Engineering & Physical Science Research Council (E, Commission of the European Communities |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology Iterative reconstruction Texture (music) Expression (mathematics) Face (geometry) 0202 electrical engineering electronic engineering information engineering Identity (object-oriented programming) 020201 artificial intelligence & image processing Computer vision Artificial intelligence business ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE International Conference on Computer Vision and Pattern Recognition CVPR |
| ISSN: | 1063-6919 |
| DOI: | 10.1109/cvpr.2017.580 |
| Popis: | 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 optimize 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. An open source implementation of our technique is released as part of the Menpo Project. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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