Multiangle feature fusion network for style transfer

Abstract

In recent years, arbitrary style transfer has gained a lot of attention from researchers. Although existing methods achieve good results, the generated images are usually biased towards styles, resulting in images with artifacts and repetitive patterns. To address the above problems, we propose a multi-angle feature fusion network for style transfer (MAFST). MAFST consists of a Multi-Angle Feature Fusion module (MAFF), a Multi-Scale Style Capture module (MSSC), multi-angle loss, and a content temporal consistency loss. MAFF can process the captured features from channel level and pixel level, and feature fusion is performed both locally and globally. MSSC processes the shallow style features and optimize generated images. To guide the model to focus on local features, we introduce a multi-angle loss. The content temporal consistency loss extends image style transfer to video style transfer. Extensive experiments have demonstrated that our proposed MAFST can effectively avoid images with artifacts and repetitive patterns. MAFST achieves advanced performance.