Flow style-aware network for arbitrary style transfer
Zhenshan Hu, Bin Ge, Chenxing Xia, Wenyan Wu, Guangao Zhou, Baotong Wang · Computers & Graphics · 2024
Researchers have recently proposed arbitrary style transfer methods based on various model frameworks. Although all of them have achieved good results, they still face the problems of insufficient stylization, artifacts and inadequate retention of content structure. In order to solve these problems, we propose a flow style-aware network (FSANet) for arbitrary style transfer, which combines a VGG network and a flow network. FSANet consists of a flow style transfer module (FSTM), a dynamic regulation attention module (DRAM), and a style feature interaction module (SFIM). The flow style transfer module uses the reversible residue block features of the flow network to create a sample feature containing the target content and style. To adapt the FSTM to VGG networks, we design the dynamic regulation attention module and exploit the sample features both at the channel and pixel levels. The style feature interaction module computes a style tensor that optimizes the fused features. Extensive qualitative and quantitative experiments demonstrate that our proposed FSANet can effectively avoid artifacts and enhance the preservation of content details while migrating style features.