{"ID":2839945,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.14291","arxiv_id":"2511.14291","title":"GEN3D: Generating Domain-Free 3D Scenes from a Single Image","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.","short_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 pr...","url_abs":"https://arxiv.org/abs/2511.14291","url_pdf":"https://arxiv.org/pdf/2511.14291v1","authors":"[\"Yuxin Zhang\",\"Ziyu Lu\",\"Hongbo Duan\",\"Keyu Fan\",\"Pengting Luo\",\"Peiyu Zhuang\",\"Mengyu Yang\",\"Houde Liu\"]","published":"2025-11-18T09:40:43Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false}