{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T13:19:55Z","timestamp":1771507195834,"version":"3.50.1"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Virtual Real."],"abstract":"<jats:p>Visual realism is fundamental to convincing Mixed Reality (MR) experiences. However, current design workflows implicitly assume that physically-based rendering parameters naturally lead to perceptually realistic results. We begin from the opposite hypothesis: physically accurate parameters and users\u2019 perceived realism are often misaligned, leading to inconsistent visual fusion and significant design overhead. To address this problem, we present AIMERS, an AI- and perception-guided MR scene design framework. First, AI-based neural inverse rendering is used to automatically estimate lighting-independent material, geometry, and illumination properties from multi-view RGB inputs, removing the need for manual material calibration. We then introduce an interactive MR perceptual interface that allows users to adjust key realism parameters during immersive viewing, enabling us to capture perception-aligned preferences across scenes. By jointly analyzing physically derived parameters and perceptual data, we derive optimal parameter intervals that best match perceived realism across different scene categories. Controlled user studies reveal a consistent mismatch between physical correctness and human perception, and demonstrate that combining AI estimation with perception-guided adjustment leads to more coherent and convincing MR visual fusion. Overall, this work establishes a perception-aligned paradigm for MR scene design, bridging the gap between physical accuracy and human perception and providing practical guidance for building realistic MR applications.<\/jats:p>","DOI":"10.3389\/frvir.2026.1733259","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T12:30:08Z","timestamp":1771504208000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AIMERS: an AI-based MR scene design system with human-centric perception optimization"],"prefix":"10.3389","volume":"7","author":[{"given":"Xiaokang","family":"Wei","sequence":"first","affiliation":[{"name":"School of Design, The Hong Kong Polytechnic University","place":["Hong Kong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqian","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Design, The Hong Kong Polytechnic University","place":["Hong Kong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ao","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Design, The Hong Kong Polytechnic University","place":["Hong Kong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Luximon","sequence":"additional","affiliation":[{"name":"School of Design, The Hong Kong Polytechnic University","place":["Hong Kong, China"]},{"name":"Laboratory for Artificial intelligence in Design (AiDLab)","place":["Hong Kong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV48922.2021.01245","article-title":"Nerd: neural reflectance decomposition from image collections","volume-title":"ICCV","author":"Boss","year":""},{"key":"B2","first-page":"10691","article-title":"Neural-PIL: neural pre-integrated lighting for reflectance decomposition","volume":"818","author":"Boss","year":"","journal-title":"Proceedings of the 35th International Conference on Neural Information Processing Systems (NIPS \u201921)"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14929","article-title":"IBL\u2010NeRF: image-Based lighting formulation of neural radiance fields","volume":"42","author":"Choi","year":"2023","journal-title":"Comput. 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