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• W2912180144 startingPage "459" @default.
• W2912180144 abstract "Face reconstruction is a popular topic in 3D vision system. However, traditional methods often depend on monocular cues, which contain few feature pixels and only use their location information while ignoring a lot of textural information. Furthermore, they are affected by the accuracy of the feature extraction method and occlusion. Here, we propose a novel facial reconstruction framework that accurately extracts the 3D shapes and poses of faces from images captured at multi-views. It extends the traditional method using the monocular bilinear model to the multi-view-based bilinear model by incorporating the feature prior constraint and the texture constraint, which are learned from multi-view images. The feature prior constraint is used as a shape prior to allowing us to estimate accurate 3D facial contours. Furthermore, the texture constraint extracts a high-precision 3D facial shape where traditional methods fail because of their limited number of feature points or the mostly texture-less and texture-repetitive nature of the input images. Meanwhile, it fully explores the implied 3D information of the multi-view images, which also enhances the robustness of the results. Additionally, the proposed method uses only two or more uncalibrated images with an arbitrary baseline, estimating calibration and shape simultaneously. A comparison with the state-of-the-art monocular bilinear model-based method shows that the proposed method has a significantly higher level of accuracy." @default.
• W2912180144 created "2019-02-21" @default.
• W2912180144 creator A5033232360 @default.
• W2912180144 creator A5046278993 @default.
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• W2912180144 date "2019-01-23" @default.
• W2912180144 modified "2023-10-16" @default.
• W2912180144 title "Three-Dimensional Face Reconstruction Using Multi-View-Based Bilinear Model" @default.
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• W2912180144 doi "https://doi.org/10.3390/s19030459" @default.
• W2912180144 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6386940" @default.
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