{"id":32201,"date":"2026-06-08T16:40:00","date_gmt":"2026-06-08T14:40:00","guid":{"rendered":"https:\/\/sano.science\/?p=32201"},"modified":"2026-07-21T16:46:51","modified_gmt":"2026-07-21T14:46:51","slug":"lumimotion-when-scene-motion-improves-light","status":"publish","type":"post","link":"https:\/\/sano.science\/lumimotion-when-scene-motion-improves-light\/","title":{"rendered":"LumiMotion: when scene motion improves light"},"content":{"rendered":"\n<p class=\"eplus-wrapper wp-block-paragraph\">At the Conference on Computer Vision and Pattern Recognition (CVPR) 2026 in Denver,<a href=\"https:\/\/sano.science\/people\/joanna-kaleta\/\" data-type=\"people\" data-id=\"12020\"> Joanna Kaleta<\/a> presented the highlighted poster \u201c<em><strong>LumiMotion: Improving Gaussian Relighting with Scene Dynamics<\/strong><\/em>\u201d, co\u2011authored with Piotr W\u00f3jcik, Kacper Marzol, Tomasz Trzci\u0144ski, Kacper Kania and Marek Kowalski, showing how scene dynamics can serve as an additional supervisory signal to achieve more physically grounded, flexible and realistic lighting in relighting pipelines based on 3D Gaussian Splatting \u2013 a technology increasingly used in computer graphics and mixed reality.<\/p>\n\n\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\"eplus-wrapper wp-block-paragraph\">CVPR is one of the world\u2019s leading conferences in computer vision and machine learning, and in 2026 it took place on 3\u20137 June in Denver, Colorado, with highlighted papers and posters representing a small fraction of accepted works that the community regards as particularly influential for the future of the field.<\/p>\n\n\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<h2 id=\"h-what-is-lumimotion\" class=\"wp-block-heading eplus-wrapper\">What is LumiMotion?<\/h2>\n\n\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\"eplus-wrapper wp-block-paragraph\">LumiMotion is a two\u2011stage inverse rendering framework whose goal is to disentangle object geometry and material from scene illumination so that lighting conditions can be realistically changed afterwards. The key idea is to treat motion as a supervisory signal that reveals the same surfaces under different lighting, helping the model understand which image variations belong to static scene structure and which are caused by changing illumination.<\/p>\n\n\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\"eplus-wrapper wp-block-paragraph\">In practice, LumiMotion improves relighting quality in dynamic scenes where traditional 3D Gaussian Splatting methods designed for static setups start to struggle, especially under complex lighting. By explicitly exploiting motion, the method achieves more consistent colors, better material appearance and more stable shadows when the direction or spectrum of light is modified.<\/p>\n\n\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\"eplus-wrapper wp-block-paragraph\">LumiMotion is rooted in computer graphics and computer vision and its core challenge \u2014 reconstructing 3D scenes while disentangling illumination from the underlying appearance of surfaces \u2014 is highly relevant to computational medicine. This is especially important in medical imaging settings such as endoscopy, where the light source is co-located with the camera and strongly shapes what the sensor observes. Methods that make lighting more realistic, controllable, and separable from tissue appearance can support more reliable medical 3D reconstruction, more robust synthetic data generation for AI models, and better simulation and visualisation tools for training, planning, and education.<\/p>\n\n\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\"eplus-wrapper wp-block-paragraph\">Joanna Kaleta\u2019s CVPR 2026 poster, developed together with Piotr W\u00f3jcik, Kacper Marzol, Tomasz Trzci\u0144ski, Kacper Kania and Marek Kowalski, illustrates how work at the intersection of academia and Sano can simultaneously advance core computer vision methods and lay technical foundations for future applications in digital health.<\/p>\n","protected":false},"excerpt":"\u2013 Joanna\u00a0Kaleta\u2019s\u00a0CVPR 2026\u00a0highlight\u00a0","author":8,"featured_media":32202,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"editor_plus_post_options":"{}","editor_plus_copied_stylings":"{}","footnotes":""},"categories":[1],"tags":[],"class_list":["post-32201","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.0 (Yoast SEO v28.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>LumiMotion: when scene motion improves light - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/lumimotion-when-scene-motion-improves-light\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" 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class=\" eplus-wrapper\">At the Conference on Computer Vision and Pattern Recognition (CVPR) 2026 in Denver,<a href=\"https:\/\/sano.science\/people\/joanna-kaleta\/\" data-type=\"people\" data-id=\"12020\"> Joanna Kaleta<\/a> presented the highlighted poster \u201c<em><strong>LumiMotion: Improving Gaussian Relighting with Scene Dynamics<\/strong><\/em>\u201d, co\u2011authored with Piotr W\u00f3jcik, Kacper Marzol, Tomasz Trzci\u0144ski, Kacper Kania and Marek Kowalski, showing how scene dynamics can serve as an additional supervisory signal to achieve more physically grounded, flexible and realistic lighting in relighting pipelines based on 3D Gaussian Splatting \u2013 a technology increasingly used in computer graphics and mixed reality.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">At the Conference on Computer Vision and Pattern Recognition (CVPR) 2026 in Denver,<a href=\"https:\/\/sano.science\/people\/joanna-kaleta\/\" data-type=\"people\" data-id=\"12020\"> Joanna Kaleta<\/a> presented the highlighted poster \u201c<em><strong>LumiMotion: Improving Gaussian Relighting with Scene Dynamics<\/strong><\/em>\u201d, co\u2011authored with Piotr W\u00f3jcik, Kacper Marzol, Tomasz Trzci\u0144ski, Kacper Kania and Marek Kowalski, showing how scene dynamics can serve as an additional supervisory signal to achieve more physically grounded, flexible and realistic lighting in relighting pipelines based on 3D Gaussian Splatting \u2013 a technology increasingly used in computer graphics and mixed reality.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-jbNwQq","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-rnkgei","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">CVPR is one of the world\u2019s leading conferences in computer vision and machine learning, and in 2026 it took place on 3\u20137 June in Denver, Colorado, with highlighted papers and posters representing a small fraction of accepted works that the community regards as particularly influential for the future of the field.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">CVPR is one of the world\u2019s leading conferences in computer vision and machine learning, and in 2026 it took place on 3\u20137 June in Denver, Colorado, with highlighted papers and posters representing a small fraction of accepted works that the community regards as particularly influential for the future of the field.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-jbNwQq","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/heading","attrs":{"anchor":"h-what-is-lumimotion","epAnimationGeneratedClass":"edplus_anim-eU3Xvy","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h2 id=\"h-what-is-lumimotion\" class=\"wp-block-heading eplus-wrapper\">What is LumiMotion?<\/h2>\n","innerContent":["\n<h2 id=\"h-what-is-lumimotion\" class=\"wp-block-heading eplus-wrapper\">What is LumiMotion?<\/h2>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-jbNwQq","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-fuxCBA","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">LumiMotion is a two\u2011stage inverse rendering framework whose goal is to disentangle object geometry and material from scene illumination so that lighting conditions can be realistically changed afterwards. The key idea is to treat motion as a supervisory signal that reveals the same surfaces under different lighting, helping the model understand which image variations belong to static scene structure and which are caused by changing illumination.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">LumiMotion is a two\u2011stage inverse rendering framework whose goal is to disentangle object geometry and material from scene illumination so that lighting conditions can be realistically changed afterwards. The key idea is to treat motion as a supervisory signal that reveals the same surfaces under different lighting, helping the model understand which image variations belong to static scene structure and which are caused by changing illumination.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-jbNwQq","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-kTmf4b","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">In practice, LumiMotion improves relighting quality in dynamic scenes where traditional 3D Gaussian Splatting methods designed for static setups start to struggle, especially under complex lighting. By explicitly exploiting motion, the method achieves more consistent colors, better material appearance and more stable shadows when the direction or spectrum of light is modified.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">In practice, LumiMotion improves relighting quality in dynamic scenes where traditional 3D Gaussian Splatting methods designed for static setups start to struggle, especially under complex lighting. By explicitly exploiting motion, the method achieves more consistent colors, better material appearance and more stable shadows when the direction or spectrum of light is modified.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-jbNwQq","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-IWcjZ2","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">LumiMotion is rooted in computer graphics and computer vision and its core challenge \u2014 reconstructing 3D scenes while disentangling illumination from the underlying appearance of surfaces \u2014 is highly relevant to computational medicine. This is especially important in medical imaging settings such as endoscopy, where the light source is co-located with the camera and strongly shapes what the sensor observes. Methods that make lighting more realistic, controllable, and separable from tissue appearance can support more reliable medical 3D reconstruction, more robust synthetic data generation for AI models, and better simulation and visualisation tools for training, planning, and education.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">LumiMotion is rooted in computer graphics and computer vision and its core challenge \u2014 reconstructing 3D scenes while disentangling illumination from the underlying appearance of surfaces \u2014 is highly relevant to computational medicine. This is especially important in medical imaging settings such as endoscopy, where the light source is co-located with the camera and strongly shapes what the sensor observes. Methods that make lighting more realistic, controllable, and separable from tissue appearance can support more reliable medical 3D reconstruction, more robust synthetic data generation for AI models, and better simulation and visualisation tools for training, planning, and education.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-jbNwQq","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-fvmN8P","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Joanna Kaleta\u2019s CVPR 2026 poster, developed together with Piotr W\u00f3jcik, Kacper Marzol, Tomasz Trzci\u0144ski, Kacper Kania and Marek Kowalski, illustrates how work at the intersection of academia and Sano can simultaneously advance core computer vision methods and lay technical foundations for future applications in digital health.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Joanna Kaleta\u2019s CVPR 2026 poster, developed together with Piotr W\u00f3jcik, Kacper Marzol, Tomasz Trzci\u0144ski, Kacper Kania and Marek Kowalski, illustrates how work at the intersection of academia and Sano can simultaneously advance core computer vision methods and lay technical foundations for future applications in digital health.<\/p>\n"]}],"meta_data":{"has_thumbnail_pattern":false,"share_on_social_media":{"has_social_media":false}},"featured_image":{"url":"https:\/\/sano.science\/wp-content\/uploads\/2026\/07\/umiMotion-Improving-Gaussian-Relighting-with-Scene-Dynamics-1024x509.png"},"main_category":{"name":"Uncategorized"},"prev_page":{"slug":"collaboration-kick-off-at-cyfronet-agh"},"next_page":{"slug":"maciej-malawski-at-the-us-poland-science-and-technology-symposium-2026"},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/posts\/32201","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/comments?post=32201"}],"version-history":[{"count":1,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/posts\/32201\/revisions"}],"predecessor-version":[{"id":32203,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/posts\/32201\/revisions\/32203"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/32202"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=32201"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/categories?post=32201"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/tags?post=32201"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}