{"type":"ScholarlyArticle","title":"FastScene: Text-Driven Fast Indoor 3D Scene Generation via Panoramic Gaussian Splatting","authors":[{"name":"Yikun Ma","slug":"yikun-ma","given":"Yikun","family":"Ma","affiliation":"sen University"},{"name":"Dandan Zhan","slug":"dandan-zhan","given":"Dandan","family":"Zhan","affiliation":"sen University"},{"name":"Zhi Jin","slug":"zhi-jin","given":"Zhi","family":"Jin","affiliation":"Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology"}],"abstract":"Text-driven 3D indoor scene generation holds broad applications, ranging from gaming and smart homes to AR/VR applications. Fast and high-fidelity scene generation is paramount for ensuring\r\nuser-friendly experiences. However, existing methods are characterized by lengthy generation processes or necessitate the intricate manual specification of motion parameters, which introduces inconvenience for users. Furthermore, these methods often rely on narrow-field viewpoint iterative generations, compromising global consistency and overall scene quality. To address these issues, we propose FastScene, a framework for fast and higher-quality 3D scene generation, while maintaining the scene consistency. Specifically, given a text prompt, we generate a panorama and estimate its depth, since the panorama encompasses information about the entire scene and exhibits explicit geometric constraints. To obtain high-quality novel views, we introduce the Coarse View Synthesis (CVS) and Progressive Novel View Inpainting (PNVI) strategies, ensuring both scene consistency and view quality. Subsequently, we utilize Multi-View Projection (MVP) to form perspective views, and apply 3D Gaussian Splatting (3DGS) for scene reconstruction. Comprehensive experiments demonstrate FastScene surpasses other methods in both generation speed and quality with better scene consistency. Notably, guided only by a text prompt, FastScene can generate a 3D scene within a mere 15 minutes, which is at least one hour faster than state-of-the-art methods, making it a paradigm for user-friendly scene generation.","doi":"10.24963/ijcai.2024/130","url":"https://ijcai.org/proceedings/2024/130","pdf":"https://ijcai.org/proceedings/2024/0130.pdf","markdown":"https://ijcai.org/proceedings/2024/130.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/130","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":130,"pages":{"first":1173,"last":1181},"keywords":["Computer Vision: CV: 3D computer vision","Computer Vision: CV: Image and video synthesis and generation","Computer Vision: CV: Multimodal learning","Computer Vision: CV: Scene analysis and understanding"],"videos":[],"references":[{"position":1,"raw":"[Barron et al., 2021] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. 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