RAST: Restorable arbitrary style transfer via multi-restoration
Arbitrary style transfer aims at reproducing the target image with provided artistic or photo-realistic styles. Even though existing approaches can successfully transfer style information, arbitrary style transfer still faces many challenges, such as the content leak issue. To be specific, the embedding of artistic style can lead to content changes. In this paper, we solve the content leak problem from the perspective of image restoration. In particular, an iterative architecture is proposed to achieve the restorable arbitrary style transfer (RAST), which can realize the transmission of both content and style information through the multi-restorations. We control the content-style balance in stylized images by the accuracy of image restoration. In order to ensure the effectiveness of the proposed RAST architecture, we design two novel loss functions: multi-restoration loss and style difference loss. In addition, we propose a new quantitative evaluation method to measure content preservation performance and style embedding performance. Comprehensive experiments comparing with state-of-the-art methods demonstrate that our proposed architecture can produce stylized images with superior performance on content preservation and style embedding.
Code (1)
Tasks
Style TransferSimilar Papers 제목 키워드 기반
RAST: Restorable Arbitrary Style Transfer
The objective of arbitrary style transfer is to apply a given artistic or photo-realistic style to a target image. Although current methods have shown some success in transferring style, arbitrary style transfer still h…
Style TransferA Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning
We present Unified Contrastive Arbitrary Style Transfer (UCAST), a novel style representation learning and transfer framework, which can fit in most existing arbitrary image style transfer models, e.g., CNN-based, ViT-ba…
Contrastive LearningRepresentation LearningStyle TransferDomain Enhanced Arbitrary Image Style Transfer via Contrastive Learning
In this work, we tackle the challenging problem of arbitrary image style transfer using a novel style feature representation learning method. A suitable style representation, as a key component in image stylization tasks…
Contrastive LearningImage StylizationRepresentation LearningStyle TransferName Your Style: An Arbitrary Artist-aware Image Style Transfer
Image style transfer has attracted widespread attention in the past few years. Despite its remarkable results, it requires additional style images available as references, making it less flexible and inconvenient. Using …
Style TransferBridging Text and Image for Artist Style Transfer via Contrastive Learning
Image style transfer has attracted widespread attention in the past few years. Despite its remarkable results, it requires additional style images available as references, making it less flexible and inconvenient. Using …
Contrastive LearningState Space ModelsStyle Transfer