Recent advancement in computer vision has significantly lowered the barriers to artistic creation. Exemplar-based image translation methods have attracted much attention due to flexibility and controllability. However, these methods hold assumptions regarding semantics or require semantic information as the input, while accurate semantics is not easy to obtain in artistic images. Besides, these methods suffer from cross-domain artifacts due to training data prior and generate imprecise structure due to feature compression in the spatial domain. In this paper, we propose an arbitrary Style Image Manipulation Network (SIM-Net), which leverages semantic-free information as guidance and a region transportation strategy in a self-supervised manner for image generation. Our method balances computational efficiency and high resolution to a certain extent. Moreover, our method facilitates zero-shot style image manipulation. Both qualitative and quantitative experiments demonstrate the superiority of our method over state-of-the-art methods.Code is available at https://github.com/SnailForce/SIM-Net.

翻译：近期计算机视觉的突破显著降低了艺术创作的门槛。基于示例的图像翻译方法因其灵活性和可控性而备受关注。然而，这些方法对语义信息存在假设要求，或需以语义信息作为输入，而艺术图像中精确的语义信息难以获取。此外，由于训练数据先验的影响，这些方法存在跨域伪影问题，同时因空间域中的特征压缩导致生成结构不精确。本文提出一种任意风格图像操控网络（SIM-Net），该网络以无语义信息作为引导，通过自监督方式结合区域迁移策略实现图像生成。我们的方法在一定程度上平衡了计算效率与高分辨率需求。此外，本方法支持零样本风格图像操控。定性与定量实验均证明，本方法优于当前最先进技术。代码开源地址：https://github.com/SnailForce/SIM-Net。