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Automatic synthesis of faces from visual attributes is an important problemin computer vision and has wide applications in law enforcement andentertainment. With the advent of deep generative convolutional neural networks(CNNs), attempts have been made to synthesize face images from attributes andtext descriptions. In this paper, we take a different approach, where weformulate the original problem as a stage-wise learning problem. We firstsynthesize the facial sketch corresponding to the visual attributes and then wereconstruct the face image based on the synthesized sketch. The proposedAttribute2Sketch2Face framework, which is based on a combination of deepConditional Variational Autoencoder (CVAE) and Generative Adversarial Networks(GANs), consists of three stages: (1) Synthesis of facial sketch fromattributes using a CVAE architecture, (2) Enhancement of coarse sketches toproduce sharper sketches using a GAN-based framework, and (3) Synthesis of facefrom sketch using another GAN-based network. Extensive experiments andcomparison with recent methods are performed to verify the effectiveness of theproposed attribute-based three stage face synthesis method.

Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs