9/21/2023 0 Comments Caricature head shapesTechnically, we propose a Multi-exaggeration Warper network to learn the distribution-level mapping from photo to facial exaggerations. Based on this assumption, we present the first exploration for unpaired CARIcature generation with Multiple Exaggerations (CariMe). In this paper, we generalize the caricature generation problem from instance-level warping prediction to distribution-level deformation modeling. This limits their ability on diverse exaggeration generation. Previous caricature generation methods are obsessed with predicting definite image warping from a given photo while ignoring the intrinsic representation and distribution for exaggerations in caricatures. Different from the generic image-to-image translation, drawing a caricature automatically is a more challenging task due to the existence of various spacial deformations. For supplementary material, seeĬaricature generation aims to translate real photos into caricatures with artistic styles and shape exaggerations while maintaining the identity of the subject. Publication date:Īugust 2021 * Accepted to SIGGRAPH 2021. We demonstrate StyleCariGAN also supports other StyleGAN-based image manipulations, such as facial expression control. Experimental results show that our StyleCariGAN generates realistic and detailed caricatures compared to the current state-of-the-art methods. We then append shape exaggeration blocks to the coarse layers of the layer-mixed model and train the blocks to create shape exaggerations while preserving the characteristic appearances of the input. Given an input photo, the layer-mixed model produces detailed color stylization for a caricature but without shape exaggerations. We first build a layer-mixed StyleGAN for photo-to-caricature style conversion by swapping fine layers of the StyleGAN for photos to the corresponding layers of the StyleGAN trained to generate caricatures. The key component of our method is shape exaggeration blocks that are used for modulating coarse layer feature maps of StyleGAN to produce desirable caricature shape exaggerations. Our framework, dubbed StyleCariGAN, automatically creates a realistic and detailed caricature from an input photo with optional controls on shape exaggeration degree and color stylization type. We present a caricature generation framework based on shape and style manipulation using StyleGAN.
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