{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:37:05Z","timestamp":1773801425872,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Video transitions are critical for ensuring temporal coherence in edited media, yet existing methods often rely on handcrafted effects or relative-scale trajectories that fail to capture the physical structure of real-world scenes. In this work, we introduce a scale-aware video transition framework that explicitly incorporates depth-aware 3D reasoning into a diffusion-based generation pipeline. Built upon a powerful I2V foundation, our method leverages single-image depth prediction to align camera motion with metric-scale geometry, enabling physically consistent transitions. To reduce reliance on precise camera inputs, we propose a bidirectional conditional control module and a progressive training strategy with conditional dropout, enhancing generalization to loosely specified or missing camera trajectories. Extensive experiments demonstrate that our approach achieves state-of-the-art performance, delivering realistic, geometrically coherent transitions across diverse scenes and applications with minimal input guidance.<\/jats:p>","DOI":"10.1609\/aaai.v40i7.37458","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:23:28Z","timestamp":1773789808000},"page":"5415-5423","source":"Crossref","is-referenced-by-count":0,"title":["UniScene-MoTion: Unified Scene &amp; Motion-aware Diffusion Transition Framework"],"prefix":"10.1609","volume":"40","author":[{"given":"Rui","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chongmian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinghe","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yehao","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Li","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\/37458\/41420","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37458\/41420","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:23:28Z","timestamp":1773789808000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/37458"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i7.37458","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]]}}}