GEN3D: Generating Domain-Free 3D Scenes from a Single Image

• URL: http://arxiv.org/abs/2511.14291v1
• Date: Tue, 18 Nov 2025 09:40:43 GMT
• Title: GEN3D: Generating Domain-Free 3D Scenes from a Single Image
• Authors: Yuxin Zhang, Ziyu Lu, Hongbo Duan, Keyu Fan, Pengting Luo, Peiyu Zhuang, Mengyu Yang, Houde Liu,
• Abstract summary: Gen3d is a novel method for generation of high-quality, wide-scope, and generic 3D scenes from a single image.<n>Experiments on diverse datasets demonstrate the strong generalization capability and superior performance of our method.
• Score: 13.128331445276764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite recent advancements in neural 3D reconstruction, the dependence on dense multi-view captures restricts their broader applicability. Additionally, 3D scene generation is vital for advancing embodied AI and world models, which depend on diverse, high-quality scenes for learning and evaluation. In this work, we propose Gen3d, a novel method for generation of high-quality, wide-scope, and generic 3D scenes from a single image. After the initial point cloud is created by lifting the RGBD image, Gen3d maintains and expands its world model. The 3D scene is finalized through optimizing a Gaussian splatting representation. Extensive experiments on diverse datasets demonstrate the strong generalization capability and superior performance of our method in generating a world model and Synthesizing high-fidelity and consistent novel views.

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