We propose a novel framework to transfer the portrait image into its correspondence with photo-realistic and cartoon style. The existing work on neural style transfer conducts impressive results on artistic style transfer; however, the lack of semantic clues will lead to the color artifacts in photo-realistic style transfer because of the complex background and noise issues. In this work, we re-define the semantics as the pixel motion field according to the color displacement between adjacent animation frames along the optical direction and initiatively propose the self-supervised semantic network (SSNet) to learn semantic maps without human inference or any priories. The SSNet shares parameters with the style transfer network; thus, the superior alternatives can preserve the semantic completeness in the styled image. To solve the content missing and blur problems common in NST, we propose the bilateral convolution block (B-block) and feature fusion strategy (F-block) for visual smoothness to meet the perceptive satisfaction. The ablation studies are provided to validate the effectiveness, and comparative experiments with the state-of-the-art baselines demonstrate the advantages of the proposed method.

我们提出了一个新颖的框架，将人像图像转换为具有照片般逼真和卡通风格的对应关系。现有的神经风格迁移工作在艺术风格迁移方面取得了令人瞩目的成果；然而，由于复杂的背景和噪声问题，缺乏语义线索将导致照片写实风格转移中的颜色伪影。在这项工作中，我们根据相邻动画帧之间沿光学方向的颜色位移将语义重新定义为像素运动场，并主动提出自监督语义网络（SSNet）来学习语义图，无需人工推理或任何先验. SSNet 与样式传输网络共享参数；因此，优越的替代方案可以保留样式图像中的语义完整性。为了解决NST中常见的内容缺失和模糊问题，我们提出了双边卷积块（B-block）和特征融合策略（F-block）来实现视觉平滑度以满足感知满意度。提供消融研究以验证有效性，并且与最先进基线的比较实验证明了所提出方法的优势。