{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/high-resolution-network-for-photorealistic","title":"High-Resolution Network for Photorealistic Style Transfer","arxiv_id":"1904.11617","date":"2019-04-25","proceeding":null,"authors":["Ming Li","Chunyang Ye","Wei Li"],"abstract":"Photorealistic style transfer aims to transfer the style of one image to\nanother, but preserves the original structure and detail outline of the content\nimage, which makes the content image still look like a real shot after the\nstyle transfer. Although some realistic image styling methods have been\nproposed, these methods are vulnerable to lose the details of the content image\nand produce some irregular distortion structures. In this paper, we use a\nhigh-resolution network as the image generation network. Compared to other\nmethods, which reduce the resolution and then restore the high resolution, our\ngeneration network maintains high resolution throughout the process. By\nconnecting high-resolution subnets to low-resolution subnets in parallel and\nrepeatedly multi-scale fusion, high-resolution subnets can continuously receive\ninformation from low-resolution subnets. This allows our network to discard\nless information contained in the image, so the generated images may have a\nmore elaborate structure and less distortion, which is crucial to the visual\nquality. We conducted extensive experiments and compared the results with\nexisting methods. The experimental results show that our model is effective and\nproduces better results than existing methods for photorealistic image\nstylization. Our source code with PyTorch framework will be publicly available\nat https://github.com/limingcv/Photorealistic-Style-Transfer","url_abs":"http://arxiv.org/abs/1904.11617v1","url_pdf":"http://arxiv.org/pdf/1904.11617v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"high-resolution-network-for-photorealistic","repo_url":"https://github.com/limingcv/Photorealistic-Style-Transfer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"high-resolution-network-for-photorealistic","repo_url":"https://github.com/divyanshu092/Photorealistic_Style_Transfer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"high-resolution-network-for-photorealistic","repo_url":"https://github.com/jarek-pawlowski/advanced-machine-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"style-transfer","task_name":"Style Transfer"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.11617","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}