{"title":"Taming diffusion model for exemplar-based image translation","authors":"Hao Ma, Jingyuan Yang, Hui Huang","doi":"10.1007/s41095-023-0371-3","DOIUrl":null,"url":null,"abstract":"<p>Exemplar-based image translation involves converting semantic masks into photorealistic images that adopt the style of a given exemplar. However, most existing GAN-based translation methods fail to produce photorealistic results. In this study, we propose a new diffusion model-based approach for generating high-quality images that are semantically aligned with the input mask and resemble an exemplar in style. The proposed method trains a conditional denoising diffusion probabilistic model (DDPM) with a SPADE module to integrate the semantic map. We then used a novel contextual loss and auxiliary color loss to guide the optimization process, resulting in images that were visually pleasing and semantically accurate. Experiments demonstrate that our method outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.</p>","PeriodicalId":37301,"journal":{"name":"Computational Visual Media","volume":null,"pages":null},"PeriodicalIF":17.3000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Visual Media","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s41095-023-0371-3","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Exemplar-based image translation involves converting semantic masks into photorealistic images that adopt the style of a given exemplar. However, most existing GAN-based translation methods fail to produce photorealistic results. In this study, we propose a new diffusion model-based approach for generating high-quality images that are semantically aligned with the input mask and resemble an exemplar in style. The proposed method trains a conditional denoising diffusion probabilistic model (DDPM) with a SPADE module to integrate the semantic map. We then used a novel contextual loss and auxiliary color loss to guide the optimization process, resulting in images that were visually pleasing and semantically accurate. Experiments demonstrate that our method outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.
期刊介绍:
Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media.
Computational Visual Media publishes articles that focus on, but are not limited to, the following areas:
• Editing and composition of visual media
• Geometric computing for images and video
• Geometry modeling and processing
• Machine learning for visual media
• Physically based animation
• Realistic rendering
• Recognition and understanding of visual media
• Visual computing for robotics
• Visualization and visual analytics
Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope.
This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.