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CityLift framework generates editable 3D urban scenes from single images

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CityLift, a novel framework for generating editable 3D urban scenes from a single image. The system addresses challenges like occlusion and perspective ambiguity by jointly learning building shape and placement. CityLift uses a scene-level spatial model and a building-level synthesis approach, incorporating generative spatial inheritance to guide a flow model for finer structure generation. To support this work, a new dataset called CityLift-5K was created, containing nearly 5,000 synthetic scenes with detailed annotations. AI

IMPACT Enables more efficient creation of detailed 3D urban environments for applications in gaming, simulation, and urban planning.

RANK_REASON The cluster contains a research paper detailing a new method for 3D scene generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CityLift framework generates editable 3D urban scenes from single images

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The cluster contains a research paper detailing a new method for 3D scene generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Haoran Gong, Jie Feng, Chenxu Wang ·

    CityLift: Compositional 3D Urban Generation from a Single Image

    arXiv:2610.32728v1 Announce Type: new Abstract: Generating editable 3D urban scenes from a single image requires detailed building assets with a coherent spatial arrangement. Occlusion and perspective ambiguity make it difficult to infer complete building geometry together with c…

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