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

翻译：现有风格迁移算法的机制是通过最小化混合损失函数，使生成图像在内容与风格上均具备高相似性。然而，此类方法无法保证视觉保真度，即生成的艺术作品应与真实作品难以区分。本文提出一种名为QuantArt的新风格迁移框架，旨在实现高视觉保真度的风格化。QuantArt通过向量量化，将生成艺术作品的潜在表征推向真实艺术作品分布的质心。通过融合量化与连续潜在表征，QuantArt能够在内容保留、风格相似度和视觉保真度方面灵活控制生成的艺术作品。多种风格迁移设置下的实验表明，与现有风格迁移方法相比，我们的QuantArt框架显著提升了视觉保真度。