TY - GEN
T1 - SceneGenesis
T2 - 40th AAAI Conference on Artificial Intelligence, AAAI 2026
AU - Zhao, Yueming
AU - Yang, Hongyu
AU - Huang, Di
N1 - Publisher Copyright:
© 2026, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2026
Y1 - 2026
N2 - Generating high-quality, controllable, and structurally consistent 3D scenes in complex multi-object environments remains a fundamental challenge. We present SceneGenesis, a unified framework that synthesizes 3D scenes by combining semantic structural priors with mesh-guided video–geometry fusion. SceneGenesis first employs large language models to convert textual descriptions into category-aware object specifications, which are transformed into structured meshes using procedural approximations and pretrained asset generators, enabling precise layout control and scalable scene construction. To obtain rich and style-controllable appearances, SceneGenesis generates multi-view video representations conditioned on the initialized structure. A mesh-guided video–geometry fusion module then consolidates video evidence with mesh priors through mesh-conditioned fragment initialization, progressive geometric refinement, and structure-aware optimization, substantially improving global geometric fidelity and visual realism. Experiments demonstrate that SceneGenesis supports flexible style variation and object-level editing while achieving strong controllability, scalability, and structural quality.
AB - Generating high-quality, controllable, and structurally consistent 3D scenes in complex multi-object environments remains a fundamental challenge. We present SceneGenesis, a unified framework that synthesizes 3D scenes by combining semantic structural priors with mesh-guided video–geometry fusion. SceneGenesis first employs large language models to convert textual descriptions into category-aware object specifications, which are transformed into structured meshes using procedural approximations and pretrained asset generators, enabling precise layout control and scalable scene construction. To obtain rich and style-controllable appearances, SceneGenesis generates multi-view video representations conditioned on the initialized structure. A mesh-guided video–geometry fusion module then consolidates video evidence with mesh priors through mesh-conditioned fragment initialization, progressive geometric refinement, and structure-aware optimization, substantially improving global geometric fidelity and visual realism. Experiments demonstrate that SceneGenesis supports flexible style variation and object-level editing while achieving strong controllability, scalability, and structural quality.
UR - https://www.scopus.com/pages/publications/105034963097
U2 - 10.1609/aaai.v40i16.38333
DO - 10.1609/aaai.v40i16.38333
M3 - 会议稿件
AN - SCOPUS:105034963097
SN - 9781577359067
SN - 9781577359067
SN - 9781577359067
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SN - 9781577359067
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SN - 9781577359067
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SN - 9781577359067
SN - 9781577359067
SN - 9781577359067
SN - 9781577359067
SN - 9781577359067
SN - 9781577359067
SN - 9781577359067
SN - 9781577359067
T3 - Proceedings of the AAAI Conference on Artificial Intelligence
SP - 13305
EP - 13313
BT - Proceedings of the AAAI Conference on Artificial Intelligence
A2 - Koenig, Sven
A2 - Jenkins, Chad
A2 - Taylor, Matthew E.
PB - Association for the Advancement of Artificial Intelligence
Y2 - 20 January 2026 through 27 January 2026
ER -