{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:39:17Z","timestamp":1773801557475,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>The increasing demand for AR\/VR applications has highlighted the need for high-quality content, such as 360\u00b0 live wallpapers. \nHowever, generating high-quality 360\u00b0 panoramic contents remains a challenging task due to the severe distortions introduced by equirectangular projection (ERP).\nExisting approaches either fine-tune pretrained diffusion models on limited ERP datasets or adopt tuning-free methods that still rely on ERP latent representations, often resulting in distracting distortions near the poles.\nIn this paper, we introduce SphereDiff, a novel approach for synthesizing 360\u00b0 static and live wallpaper with state-of-the-art diffusion models without additional tuning. \nWe define a spherical latent representation that ensures consistent quality across all perspectives, including near the poles.\nThen, we extend MultiDiffusion to spherical latent representation and propose a dynamic spherical latent sampling method to enable direct use of pretrained diffusion models.\nMoreover, we introduce distortion-aware weighted averaging to further improve the generation quality.\nOur method outperforms existing approaches in generating 360\u00b0 static and live wallpaper, making it a robust solution for immersive AR\/VR applications.<\/jats:p>","DOI":"10.1609\/aaai.v40i10.37779","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:42:21Z","timestamp":1773790941000},"page":"8305-8313","source":"Crossref","is-referenced-by-count":0,"title":["SphereDiff: Tuning-free 360\u00b0 Static and Dynamic Panorama Generation via Spherical Latent Representation"],"prefix":"10.1609","volume":"40","author":[{"given":"Minho","family":"Park","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taewoong","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jooyeol","family":"Yun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sungwon","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaegul","family":"Choo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37779\/41741","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37779\/41741","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:42:21Z","timestamp":1773790941000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/37779"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i10.37779","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}