Coarse-to-fine multiview 3d face reconstruction using multiple geometrical features

Dai, Peng; Wang, Xue; Zhang, Weihang

doi:10.1007/s11042-016-4325-y

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…Furthermore, in order to verify the precision of our method, we compared it with other state-of-the-art monocular- [41,42,43] and multi-view-based [30,37,44] approaches. According to the quantitative and qualitative evaluation results shown in Table 2 and Figure 6, it can be concluded that the proposed method is successful in the following aspects:Because the feature pixels extracted from multi-viewpoints are used as the feature prior constraint, the proposed method estimates accurate contours of the generated 3D face mesh even with the effect of occlusion, where previous methods often failed.…”

Section: Resultsmentioning

confidence: 99%

“…While existing techniques separate shape estimation from facial tracking, their framework jointly optimizes 3D constraints and provides consistent mesh parameterization. Dai et al [37] propose a novel “coarse-to-fine” multi-view 3D face reconstruction method by taking advantage of the complementarity between facial feature points and occluding contours. Multi-view face images with visual angle differences were employed to calculate the 3D coordinates of facial feature points to generate 3D face models.…”

Section: Previous Workmentioning

confidence: 99%

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Three-Dimensional Face Reconstruction Using Multi-View-Based Bilinear Model

Tian

Liu

Wei

2019

Sensors

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Three-Dimensional Face Reconstruction Using Multi-View-Based Bilinear Model

Tian

Liu

Wei

2019

Sensors

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“…Lin et al 17 propose a method for visual sensor networks where the amount of required facial feature points is significantly reduced using self‐adaptive morphable models. Dai et al 18 propose a coarse‐to‐fine multi‐view 3D face reconstruction method by taking advantage of the complementarity between facial feature points and occluding contours.…”

Section: Related Workmentioning

confidence: 99%

Laplacian face blending

Mazala

Esperança

Marroquim

2022

Computer Animation & Virtual

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Designing realistic tridimensional facial models is a challenging task, not only due to the effort and artistic abilities required but also because human visual perception is very tuned to the processing of facial features. For this reason, rather than creating face models from scratch, artists usually start from a scanned model of a real person. In this work, we present a novel method for blending human faces in order to create a new one. In a nutshell, our proposal uses Laplacian smoothing to segregate layers of details from one or more faces, which are then integrated into a base face with the help of an interactive and visual editor. In particular, our method supports blending multiple faces and multiple sub-regions in those faces. Since our approach is intuitive and relatively easy to implement, it can be integrated into artistic pipelines aiming at designing human face models from preexisting ones.

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Three-dimensional face reconstruction of static images and computer standardization issues

Zhao

2021

Soft Comput

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Coarse-to-fine multiview 3d face reconstruction using multiple geometrical features

Cited by 5 publications

References 39 publications

Three-Dimensional Face Reconstruction Using Multi-View-Based Bilinear Model

Three-Dimensional Face Reconstruction Using Multi-View-Based Bilinear Model

Laplacian face blending

Three-dimensional face reconstruction of static images and computer standardization issues

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