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Instance based photo cartoonization is one of the challenging imagestylization tasks which aim at transforming realistic photos into cartoon styleimages while preserving the semantic contents of the photos. State-of-the-artDeep Neural Networks (DNNs) methods still fail to produce satisfactory resultswith input photos in the wild, especially for photos which have high contrastand full of rich textures. This is due to that: cartoon style images tend tohave smooth color regions and emphasized edges which are contradict torealistic photos which require clear semantic contents, i.e., textures, shapesetc. Previous methods have difficulty in satisfying cartoon style textures andpreserving semantic contents at the same time. In this work, we propose a novel"CartoonRenderer" framework which utilizing a single trained model to generatemultiple cartoon styles. In a nutshell, our method maps photo into a featuremodel and renders the feature model back into image space. In particular,cartoonization is achieved by conducting some transformation manipulation inthe feature space with our proposed Soft-AdaIN. Extensive experimental resultsshow our method produces higher quality cartoon style images than prior arts,with accurate semantic content preservation. In addition, due to the decouplingof whole generating process into "Modeling-Coordinating-Rendering" parts, ourmethod could easily process higher resolution photos, which is intractable forexisting methods.

CartoonRenderer: An Instance-based Multi-Style Cartoon Image Translator