{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:03:56Z","timestamp":1782846236241,"version":"3.54.5"},"reference-count":0,"publisher":"ECMS","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,23]]},"abstract":"<jats:p>This research investigates the state-of-the-art of generative AI for the automated synthesis of physically accurate materiality in spatial 3D models. By addressing two core research questions, the study reveals that generative algorithms can effectively synthesize a wide array of digital-visual, digital-tactile, and procedural material properties. These encompass standard Physically Based Rendering (PBR) maps and Spatially Varying Bidirectional Reflectance Distribution Function (SVBRDF) models, alongside perceptual tactile attributes like softness and friction, enabling multi-sensory experiences via haptic devices. The algorithmic landscape is analyzed through Generative Adversarial Networks, Diffusion Models, and Transformers, underscoring a paradigm shift toward multimodal architectures and resolution-independent procedural node graphs. Ultimately, this state-of-the-art findings demonstrate that the convergence of generative technologies may enable the creation of high-fidelity 3D materials, addressing the growing demand across industries such as gaming, robotics, film, and product design for more efficient, controllable, and automated 3D content generation techniques.<\/jats:p>","DOI":"10.7148\/2026-0834","type":"proceedings-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:08:43Z","timestamp":1782842923000},"page":"834-842","source":"Crossref","is-referenced-by-count":0,"title":["Generative ai for automated materiality synthesis in spatial 3d models: state-of-the-art"],"prefix":"10.7148","author":[{"given":"Donata","family":"Sermuksne","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Filippo","family":"Sanfilippo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Algirdas","family":"Noreika","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4144","published-online":{"date-parts":[[2026,6,23]]},"event":{"name":"40th ECMS International Conference on Modelling and Simulation"},"container-title":["ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina"],"original-title":[],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:08:50Z","timestamp":1782842930000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.scs-europe.net\/dlib\/2026\/ecms2026acceptedpapers\/0834_sstmsv_ecms2026_0072.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7148\/2026-0834","relation":{},"subject":[],"published":{"date-parts":[[2026,6,23]]}}}