Photo-to-Caricature Translation on Faces in the Wild
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Recently, image-to-image translation has been made much progress owing to the success of conditional Generative Adversarial Networks (cGANs). However, it's still very challenging for translation tasks with the requirement of high-level visual information conversion, such as photo-to-caricature translation that requires satire, exaggeration, lifelikeness and artistry. We present an approach for learning to translate faces in the wild from the source photo domain to the target caricature domain with different styles, which can also be used for other high-level image-to-image translation tasks. In order to capture global structure with local statistics while translation, we design a dual pathway model of cGAN with one global discriminator and one patch discriminator. Beyond standard convolution (Conv), we propose a new parallel convolution (ParConv) to construct Parallel Convolutional Neural Networks (ParCNNs) for both global and patch discriminators, which can combine the information from previous layer with the current layer. For generator, we provide three more extra losses in association with adversarial loss to constrain consistency for generated output itself and with the target. Also the style can be controlled by the input style info vector. Experiments on photo-to-caricature translation of faces in the wild show considerable performance gain of our proposed method over state-of-the-art translation methods as well as its potential real applications.
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