  In recent years, conditional image synthesis has attracted growing attention
due to its controllability in the image generation process. Although recent
works have achieved realistic results, most of them have difficulty handling
fine-grained styles with subtle details. To address this problem, a novel
normalization module, named Detailed Region-Adaptive Normalization~(DRAN), is
proposed. It adaptively learns both fine-grained and coarse-grained style
representations. Specifically, we first introduce a multi-level structure,
Spatiality-aware Pyramid Pooling, to guide the model to learn coarse-to-fine
features. Then, to adaptively fuse different levels of styles, we propose
Dynamic Gating, making it possible to adaptively fuse different levels of
styles according to different spatial regions. Finally, we collect a new makeup
dataset (Makeup-Complex dataset) that contains a wide range of complex makeup
styles with diverse poses and expressions. To evaluate the effectiveness and
show the general use of our method, we conduct a set of experiments on makeup
transfer and semantic image synthesis. Quantitative and qualitative experiments
show that equipped with DRAN, simple baseline models are able to achieve
promising improvements in complex style transfer and detailed texture
synthesis. Both the code and the proposed dataset will be available at
https://github.com/Yueming6568/DRAN-makeup.git.
