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| Hauptverfasser: | , , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2502.05874 |
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| _version_ | 1866910894348304384 |
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| author | Yang, Zhifei Lu, Keyang Zhang, Chao Qi, Jiaxing Jiang, Hanqi Ma, Ruifei Yin, Shenglin Xu, Yifan Xing, Mingzhe Xiao, Zhen Long, Jieyi Zhai, Guangyao |
| author_facet | Yang, Zhifei Lu, Keyang Zhang, Chao Qi, Jiaxing Jiang, Hanqi Ma, Ruifei Yin, Shenglin Xu, Yifan Xing, Mingzhe Xiao, Zhen Long, Jieyi Zhai, Guangyao |
| contents | Controllable 3D scene generation has extensive applications in virtual reality and interior design, where the generated scenes should exhibit high levels of realism and controllability in terms of geometry. Scene graphs provide a suitable data representation that facilitates these applications. However, current graph-based methods for scene generation are constrained to text-based inputs and exhibit insufficient adaptability to flexible user inputs, hindering the ability to precisely control object geometry. To address this issue, we propose MMGDreamer, a dual-branch diffusion model for scene generation that incorporates a novel Mixed-Modality Graph, visual enhancement module, and relation predictor. The mixed-modality graph allows object nodes to integrate textual and visual modalities, with optional relationships between nodes. It enhances adaptability to flexible user inputs and enables meticulous control over the geometry of objects in the generated scenes. The visual enhancement module enriches the visual fidelity of text-only nodes by constructing visual representations using text embeddings. Furthermore, our relation predictor leverages node representations to infer absent relationships between nodes, resulting in more coherent scene layouts. Extensive experimental results demonstrate that MMGDreamer exhibits superior control of object geometry, achieving state-of-the-art scene generation performance. Project page: https://yangzhifeio.github.io/project/MMGDreamer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_05874 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MMGDreamer: Mixed-Modality Graph for Geometry-Controllable 3D Indoor Scene Generation Yang, Zhifei Lu, Keyang Zhang, Chao Qi, Jiaxing Jiang, Hanqi Ma, Ruifei Yin, Shenglin Xu, Yifan Xing, Mingzhe Xiao, Zhen Long, Jieyi Zhai, Guangyao Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Controllable 3D scene generation has extensive applications in virtual reality and interior design, where the generated scenes should exhibit high levels of realism and controllability in terms of geometry. Scene graphs provide a suitable data representation that facilitates these applications. However, current graph-based methods for scene generation are constrained to text-based inputs and exhibit insufficient adaptability to flexible user inputs, hindering the ability to precisely control object geometry. To address this issue, we propose MMGDreamer, a dual-branch diffusion model for scene generation that incorporates a novel Mixed-Modality Graph, visual enhancement module, and relation predictor. The mixed-modality graph allows object nodes to integrate textual and visual modalities, with optional relationships between nodes. It enhances adaptability to flexible user inputs and enables meticulous control over the geometry of objects in the generated scenes. The visual enhancement module enriches the visual fidelity of text-only nodes by constructing visual representations using text embeddings. Furthermore, our relation predictor leverages node representations to infer absent relationships between nodes, resulting in more coherent scene layouts. Extensive experimental results demonstrate that MMGDreamer exhibits superior control of object geometry, achieving state-of-the-art scene generation performance. Project page: https://yangzhifeio.github.io/project/MMGDreamer. |
| title | MMGDreamer: Mixed-Modality Graph for Geometry-Controllable 3D Indoor Scene Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.05874 |