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An Image Representation for the 3D Face Synthesis

With the rapid development of the display technologies, 3D shape data is becoming another important media kind. However, most of the existing 3D shape acquisition methods are either expensive or expertise-dependent. In this paper, we present an image representation for the 3D faces to bridge the gap between the feature-lacking 3D shapes and the powerful deep neural network learning tools. To achieve this, with the training set, we first extract the radial curves for each 3D face, and reform the curves into an image matrix, which enable to apply the classical Generative Adversarial Network model for the image synthesis. Finally, we propose a refining process to transform the output images into 3D synthetic faces. Our experimental results demonstrate the capability of our method which can correctly reflect the affinities among the different facial expressions and can generate the 3D faces.