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Exploiting Spatial Relation for Reducing Distortion in Style Transfer
The power of convolutional neural networks in arbitrary style transfer has been amply demonstrated; however, existing stylization methods tend to generate spatially inconsistent results with noticeable artifacts. One solution to this problem involves the application of a segmentation mask or affinity-based image matting to preserve spatial information related to image content. The main idea of this work is to model spatial relation between content image pixels and thus to maintain this relationship in stylization for reducing artifacts. The proposed network architecture is called spatial relation-augmented VGG (SRVGG), in which long-range spatial dependency is modeled by a spatial relation module. Based on this spatial information extracted from SRVGG, we design a novel relation loss which can minimize the difference of spatial dependency between content images and stylizations. We evaluate the proposed framework on both optimization-based and feedforward-based style transfer methods. The effectiveness of SRVGG in stylization is demonstrated by generating stylized images of high quality and spatial consistency without the need for segmentation masks or affinity-based image matting. The quantitative evaluation also suggests that the proposed framework achieve better performance compared with other methods.