{"id":1368,"date":"2026-01-12T11:13:24","date_gmt":"2026-01-12T10:13:24","guid":{"rendered":"https:\/\/www.ideas.edu.pl\/?post_type=publikacje&#038;p=1368"},"modified":"2026-01-12T11:13:26","modified_gmt":"2026-01-12T10:13:26","slug":"hypernerfgan-hypernetwork-approach-to-3d-nerf-gan","status":"publish","type":"publikacje","link":"https:\/\/www.ideas.edu.pl\/en\/publikacje\/hypernerfgan-hypernetwork-approach-to-3d-nerf-gan\/","title":{"rendered":"HyperNeRFGAN: Hypernetwork approach to 3D NeRF GAN"},"content":{"rendered":"<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>The recent surge in popularity of deep generative models for 3D objects has highlighted the need for more efficient training methods, particularly given the difficulties associated with training with conventional 3D representations, such as voxels or point clouds. Neural Radiance Fields (NeRFs), which provide the current benchmark in terms of quality for the generation of novel views of complex 3D scenes from a limited set of 2D images, represent a promising solution to this challenge. However, the training of these models requires the knowledge of the respective camera positions from which the images were viewed. In this paper, we overcome this limitation by introducing HyperNeRFGAN, a Generative Adversarial Network (GAN) architecture employing a hypernetwork paradigm to transform a Gaussian noise into the weights of a NeRF architecture that does not utilize viewing directions in its training phase. Consequently, as evidenced by the findings of our experimental study, the proposed model, despite its notable simplicity in comparison to existing state-of-the-art alternatives, demonstrates superior performance on a diverse range of image datasets where camera position estimation is challenging, particularly in the context of medical data....<\/p>\n<\/blockquote>\n\n\n\n<p>Authors: Adam Kania, Artur Kasymov, Jakub Ko\u015bciukiewicz, Artur G\u00f3rak, Marcin Mazur, Maciej Zi\u0119ba, Przemys\u0142aw Spurek<\/p>\n\n\n\n<div style=\"height:64px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-group is-layout-grid wp-container-core-group-is-layout-e2bd5cb0 wp-block-group-is-layout-grid\"><\/div>\n\n\n\n<div class=\"wp-block-group is-layout-grid wp-container-core-group-is-layout-e2bd5cb0 wp-block-group-is-layout-grid\"><\/div>","protected":false},"template":"","nazwa-konferencji":[33],"rodzaj-publikacji":[13],"rok-publikacji":[14],"class_list":["post-1368","publikacje","type-publikacje","status-publish","hentry","nazwa-konferencji-konferencja-dssa","rodzaj-publikacji-artykul-konferencyjny","rok-publikacji-14"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>HyperNeRFGAN: Hypernetwork approach to 3D NeRF GAN &#8226; IDEAS Instytut Badawczy<\/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:\/\/www.ideas.edu.pl\/en\/publikacje\/hypernerfgan-hypernetwork-approach-to-3d-nerf-gan\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"HyperNeRFGAN: Hypernetwork approach to 3D NeRF GAN &#8226; IDEAS Instytut Badawczy\" \/>\n<meta property=\"og:description\" content=\"The recent surge in popularity of deep generative models for 3D objects has highlighted the need for more efficient training methods, particularly given the difficulties associated with training with conventional 3D representations, such as voxels or point clouds. Neural Radiance Fields (NeRFs), which provide the current benchmark in terms of quality for the generation of [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.ideas.edu.pl\/en\/publikacje\/hypernerfgan-hypernetwork-approach-to-3d-nerf-gan\/\" \/>\n<meta property=\"og:site_name\" content=\"IDEAS Instytut Badawczy\" \/>\n<meta property=\"article:modified_time\" content=\"2026-01-12T10:13:26+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.ideas.edu.pl\/wp-content\/uploads\/feature-image-home.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1800\" \/>\n\t<meta property=\"og:image:height\" content=\"945\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.ideas.edu.pl\\\/publikacje\\\/hypernerfgan-hypernetwork-approach-to-3d-nerf-gan\\\/\",\"url\":\"https:\\\/\\\/www.ideas.edu.pl\\\/publikacje\\\/hypernerfgan-hypernetwork-approach-to-3d-nerf-gan\\\/\",\"name\":\"HyperNeRFGAN: Hypernetwork approach to 3D NeRF GAN &#8226; IDEAS Instytut Badawczy\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.ideas.edu.pl\\\/#website\"},\"datePublished\":\"2026-01-12T10:13:24+00:00\",\"dateModified\":\"2026-01-12T10:13:26+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.ideas.edu.pl\\\/publikacje\\\/hypernerfgan-hypernetwork-approach-to-3d-nerf-gan\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.ideas.edu.pl\\\/publikacje\\\/hypernerfgan-hypernetwork-approach-to-3d-nerf-gan\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.ideas.edu.pl\\\/publikacje\\\/hypernerfgan-hypernetwork-approach-to-3d-nerf-gan\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Strona g\u0142\u00f3wna\",\"item\":\"https:\\\/\\\/www.ideas.edu.pl\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Publikacje\",\"item\":\"https:\\\/\\\/www.ideas.edu.pl\\\/publikacje\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"HyperNeRFGAN: Hypernetwork approach to 3D NeRF GAN\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/www.ideas.edu.pl\\\/#website\",\"url\":\"https:\\\/\\\/www.ideas.edu.pl\\\/\",\"name\":\"IDEAS Instytut Badawczy\",\"description\":\"Pa\u0144stwowa jednostka badawczo-naukowa\",\"publisher\":{\"@id\":\"https:\\\/\\\/www.ideas.edu.pl\\\/#organization\"},\"alternateName\":\"IDEAS\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/www.ideas.edu.pl\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/www.ideas.edu.pl\\\/#organization\",\"name\":\"IDEAS Instytut Badawczy\",\"url\":\"https:\\\/\\\/www.ideas.edu.pl\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.ideas.edu.pl\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/www.ideas.edu.pl\\\/wp-content\\\/uploads\\\/e90241d6e73025d0d829abc28d67cb84.svg\",\"contentUrl\":\"https:\\\/\\\/www.ideas.edu.pl\\\/wp-content\\\/uploads\\\/e90241d6e73025d0d829abc28d67cb84.svg\",\"width\":152,\"height\":43,\"caption\":\"IDEAS Instytut Badawczy\"},\"image\":{\"@id\":\"https:\\\/\\\/www.ideas.edu.pl\\\/#\\\/schema\\\/logo\\\/image\\\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"HyperNeRFGAN: Hypernetwork approach to 3D NeRF GAN &#8226; IDEAS Instytut Badawczy","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.ideas.edu.pl\/en\/publikacje\/hypernerfgan-hypernetwork-approach-to-3d-nerf-gan\/","og_locale":"en_US","og_type":"article","og_title":"HyperNeRFGAN: Hypernetwork approach to 3D NeRF GAN &#8226; IDEAS Instytut Badawczy","og_description":"The recent surge in popularity of deep generative models for 3D objects has highlighted the need for more efficient training methods, particularly given the difficulties associated with training with conventional 3D representations, such as voxels or point clouds. 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