We introduce a simple and versatile framework for image-to-image translation.
We unearth the importance of normalization layers, and provide a carefully
designed two-stream generative model with newly proposed feature
transformations in a coarse-to-fine fashion. This allows multi-scale semantic
structure information and style representation to be effectively captured and
fused by the network, permitting our method to scale to various tasks in both
unsupervised and supervised settings. No additional constraints (e.g., cycle
consistency) are needed, contributing to a very clean and simple method.
Multi-modal image synthesis with arbitrary style control is made possible. A
systematic study compares the proposed method with several state-of-the-art
task-specific baselines, verifying its effectiveness in both perceptual quality
and quantitative evaluations.

提出了一种简单而通用的图像到图像翻译框架，利用归一化层和新提出的特征变换，实现了多模态图像合成并具备任意风格控制的能力，进行了与多个先进方法的比较，证明了其在感知质量和定量评估方面的有效性。