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The mechanism of existing style transfer algorithms is by minimizing a hybridloss function to push the generated image toward high similarities in bothcontent and style. However, this type of approach cannot guarantee visualfidelity, i.e., the generated artworks should be indistinguishable from realones. In this paper, we devise a new style transfer framework called QuantArtfor high visual-fidelity stylization. QuantArt pushes the latent representationof the generated artwork toward the centroids of the real artwork distributionwith vector quantization. By fusing the quantized and continuous latentrepresentations, QuantArt allows flexible control over the generated artworksin terms of content preservation, style similarity, and visual fidelity.Experiments on various style transfer settings show that our QuantArt frameworkachieves significantly higher visual fidelity compared with the existing styletransfer methods.

QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity