融合UV位置图与CGAN的单图像大视角三维彩色人脸重建

Large-View 3D Color Face Reconstruction from Dingle Image via UV Location Map and CGAN

基于图像的物体三维重建一直是计算机视觉领域的研究热点.与二维人脸图像相比,三维人脸模型能够承载更多的信息从而具有更广泛的应用前景,如更精准的身份信息识别标志、更准确的情感表达媒介等.为了从单幅大视角的二维人脸图像中重建出具有真实感的三维彩色人脸模型,提出一种结构简单但有效的算法.首先设计一个编码-解码网络,从原始RGB图像生成并记录完整的三维人脸信息的二维UV位置图;然后使用一个卷积神经网络从中重塑出三维人脸;最后考虑人脸大视角时的自遮挡情况,进一步通过条件生成对抗网络补全UV纹理图的缺失.使用Stirling/ESRC 3D Face Database与其他三维人脸重建的算法进行对比实验,结果表明,所提算法能够实现更高的重建精度,特别是在大视角人脸图像重建应用中,即使在复杂环境下也可以获得完整和真实的三维人脸模型.

Image-based 3D reconstruction has been a hot topic in computer vision research.Since 3D face model can convey more information,it possesses expansive application prospects in comparison with the 2D face image,such as more precise identity information recognition signs,more accurate emotional expression media,etc.In order to reconstruct a realistic 3D color face model from a single large-view 2D face image,a simple but effective method is proposed.First,the encoder-decoder network is applied to generate the 2D UV location map from the original RGB image.Then a simple convolution neural network is applied to regress the 3D face from the UV location map.Finally,a network based on conditional generative adversarial networks(CGAN)is proposed to make up for the missing of UV texture caused by self-occlusion in the case of large pose.Compared with existing 3D face reconstruction methods in Stirling/ESRC 3D Face Database,proposed method can improve the accuracy.More important,a complete and realistic 3D face model can be reconstructed from the large-view face image even in the increasingly complex background.