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|a 10.1109/TPAMI.2025.3620403
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|a eng
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|a Li, Haoran
|e verfasserin
|4 aut
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1 |
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|a DreamScene
|b 3D Gaussian-based End-to-end Text-to-3D Scene Generation
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|a Date Revised 15.10.2025
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|a Citation Status Publisher
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|a Generating 3D scenes from natural language holds great promise for applications in gaming, film, and design. However, existing methods struggle with automation, 3D consistency, and fine-grained control. We present DreamScene, an end-to-end framework for high-quality and editable 3D scene generation from text or dialogue. DreamScene begins with a scene planning module, where a GPT-4 agent infers object semantics and spatial constraints to construct a hybrid graph. A graph-based placement algorithm then produces a structured, collision-free layout. Based on this layout, Formation Pattern Sampling (FPS) generates object geometry using multi-timestep sampling and reconstructive optimization, enabling fast and realistic synthesis. To ensure global consistent, DreamScene employs a progressive camera sampling strategy tailored to both indoor and outdoor settings. Finally, the system supports fine-grained scene editing, including object movement, appearance changes, and 4D dynamic motion. Experiments demonstrate that DreamScene surpasses prior methods in quality, consistency, and flexibility, offering a practical solution for open-domain 3D content creation. Code and demos are available at https://dreamscene-project.github.io
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|a Tian, Yuli
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1 |
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|a Lan, Kun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liao, Yong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Lin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hui, Pan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Peng Yuan
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|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g PP(2025) vom: 15. Okt.
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|u http://dx.doi.org/10.1109/TPAMI.2025.3620403
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