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Conventional real-time rendering engines sacrifice visual fidelity for interactive performance, while image generation using path-tracing techniques can be exceedingly time-consuming. In this article, we introduce RenderGAN, a deep learning-based solution designed to address this critical challenge in real-time rendering. RenderGAN uses G-Buffers and information from a real-time rendering engine as inputs to produce output images with exceptional visual fidelity. Its encoder\u2013decoder architecture, trained using the Generative Adversarial Network (GAN) framework with perceptual loss, enhances image realism. To evaluate RenderGAN\u2019s effectiveness, we quantitatively compare the generated images with those of a path-tracing engine, obtaining a remarkable Universal Image Quality Index (UIQI) value of 0.898. RenderGAN\u2019s open source nature fosters collaboration, driving advancements in real-time computer graphics and rendering techniques. By bridging the gap between real-time and path-tracing rendering, RenderGAN opens new horizons for accelerated image generation, inspiring innovation and unlocking the full potential of real-time visual experiences. Project page:\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/marcomameli1992\/RenderNet\">https:\/\/github.com\/marcomameli1992\/RenderNet<\/jats:ext-link>\n          <\/jats:p>","DOI":"10.1145\/3712263","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T16:33:41Z","timestamp":1737131621000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["RenderGAN: Enhancing Real-time Rendering Efficiency with Deep Learning"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5269-9939","authenticated-orcid":false,"given":"Marco","family":"Mameli","sequence":"first","affiliation":[{"name":"Dipartimento di Ingegneria dell\u2019 Informazione (DII), Universit\u00e0 Politecnica delle Marche, Ancona, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5523-7174","authenticated-orcid":false,"given":"Marina","family":"Paolanti","sequence":"additional","affiliation":[{"name":"University of Macerata, Macerata, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5281-9200","authenticated-orcid":false,"given":"Adriano","family":"Mancini","sequence":"additional","affiliation":[{"name":"Universit\u00e0 Politecnica delle Marche, Ancona, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5709-2159","authenticated-orcid":false,"given":"Primo","family":"Zingaretti","sequence":"additional","affiliation":[{"name":"Universit\u00e0 Politecnica delle Marche, Ancona, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9160-834X","authenticated-orcid":false,"given":"Roberto","family":"Pierdicca","sequence":"additional","affiliation":[{"name":"Universit\u00e0 Politecnica delle Marche, Dipartimento di Ingegneria Civile, Edile e dell\u2019 Architettura (DICEA), Ancona, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,3,7]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1","article-title":"Role and application of computer graphics","author":"Feruza Islamovna Mamurova","year":"2023","unstructured":"Mamurova Feruza Islamovna, Nadira Sharifovna Khadjaeva, and Elena Vladimirovna Kadirova. 2023. 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