{"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-daytime-translation-without","title":"High-Resolution Daytime Translation Without Domain Labels","arxiv_id":"2003.08791","date":"2020-03-19","proceeding":"CVPR 2020 6","authors":["Ivan Anokhin","Pavel Solovev","Denis Korzhenkov","Alexey Kharlamov","Taras Khakhulin","Alexey Silvestrov","Sergey Nikolenko","Victor Lempitsky","Gleb Sterkin"],"abstract":"Modeling daytime changes in high resolution photographs, e.g., re-rendering the same scene under different illuminations typical for day, night, or dawn, is a challenging image manipulation task. We present the high-resolution daytime translation (HiDT) model for this task. HiDT combines a generative image-to-image model and a new upsampling scheme that allows to apply image translation at high resolution. The model demonstrates competitive results in terms of both commonly used GAN metrics and human evaluation. Importantly, this good performance comes as a result of training on a dataset of still landscape images with no daytime labels available. Our results are available at https://saic-mdal.github.io/HiDT/.","url_abs":"https://arxiv.org/abs/2003.08791v2","url_pdf":"https://arxiv.org/pdf/2003.08791v2.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-daytime-translation-without","repo_url":"https://github.com/saic-mdal/HiDT","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"image-manipulation","task_name":"Image Manipulation"},{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"multimodal-unsupervised-image-to-image","task_name":"Multimodal Unsupervised Image-To-Image Translation"},{"task_slug":"style-transfer","task_name":"Style Transfer"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"unsupervised-image-to-image-translation","task_name":"Unsupervised Image-To-Image Translation"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2003.08791","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}