{"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/microglam-microscopic-skin-image-dataset-with","title":"MicroGlam: Microscopic Skin Image Dataset with Cosmetics","arxiv_id":"2401.05339","date":"2023-11-29","proceeding":null,"authors":["Toby Chong","Alina Chadwick","I-Chao Shen","Haoran Xie","Takeo Igarashi"],"abstract":"In this paper, we present a cosmetic-specific skin image dataset. It consists of skin images from $45$ patches ($5$ skin patches each from $9$ participants) of size $8mm^*8mm$ under three cosmetic products (i.e., foundation, blusher, and highlighter). We designed a novel capturing device inspired by Light Stage. Using the device, we captured over $600$ images of each skin patch under diverse lighting conditions in $30$ seconds. We repeated the process for the same skin patch under three cosmetic products. Finally, we demonstrate the viability of the dataset with an image-to-image translation-based pipeline for cosmetic rendering and compared our data-driven approach to an existing cosmetic rendering method.","url_abs":"https://arxiv.org/abs/2401.05339v1","url_pdf":"https://arxiv.org/pdf/2401.05339v1.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":"microglam-microscopic-skin-image-dataset-with","repo_url":"https://github.com/tobyclh/microglam","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}