Automated Deep Photo Style Transfer
Photorealism is a complex concept that cannot easily be formulated mathematically. Deep Photo Style Transfer is an attempt to transfer the style of a reference image to a content image while preserving its photorealism. This is achieved by introducing a constraint that prevents distortions in the content image and by applying the style transfer independently for semantically different parts of the images. In addition, an automated segmentation process is presented that consists of a neural network based segmentation method followed by a semantic grouping step. To further improve the results a measure for image aesthetics is used and elaborated. If the content and the style image are sufficiently similar, the result images look very realistic. With the automation of the image segmentation the pipeline becomes completely independent from any user interaction, which allows for new applications.
Code (1)
Tasks
Image SegmentationSegmentationSemantic SegmentationStyle TransferMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
UPST-NeRF: Universal Photorealistic Style Transfer of Neural Radiance Fields for 3D Scene
3D scenes photorealistic stylization aims to generate photorealistic images from arbitrary novel views according to a given style image while ensuring consistency when rendering from different viewpoints. Some existing s…
NeRFStyle TransferNLUT: Neural-based 3D Lookup Tables for Video Photorealistic Style Transfer
Video photorealistic style transfer is desired to generate videos with a similar photorealistic style to the style image while maintaining temporal consistency. However, existing methods obtain stylized video sequences b…
8kStyle TransferDeep Photo Style Transfer
This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style. Our approach builds upon the recent work on p…
Style TransferImage Style Transfer: from Artistic to Photorealistic
The rapid advancement of deep learning has significantly boomed the development of photorealistic style transfer. In this review, we reviewed the development of photorealistic style transfer starting from artistic style …
Deep LearningStyle TransferUniversal Photorealistic Style Transfer: A Lightweight and Adaptive Approach
Photorealistic style transfer aims to apply stylization while preserving the realism and structure of input content. However, existing methods often encounter challenges such as color tone distortions, dependency on pair…
GPUStyle TransferSuper-ResolutionVideo Style Transfer