HoloScene: Simulation-Ready Interactive 3D Worlds from a Single Video
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911195555954688 |
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| author | Xia, Hongchi Lin, Chih-Hao Hsu, Hao-Yu Leboutet, Quentin Gao, Katelyn Paulitsch, Michael Ummenhofer, Benjamin Wang, Shenlong |
| author_facet | Xia, Hongchi Lin, Chih-Hao Hsu, Hao-Yu Leboutet, Quentin Gao, Katelyn Paulitsch, Michael Ummenhofer, Benjamin Wang, Shenlong |
| contents | Digitizing the physical world into accurate simulation-ready virtual environments offers significant opportunities in a variety of fields such as augmented and virtual reality, gaming, and robotics. However, current 3D reconstruction and scene-understanding methods commonly fall short in one or more critical aspects, such as geometry completeness, object interactivity, physical plausibility, photorealistic rendering, or realistic physical properties for reliable dynamic simulation. To address these limitations, we introduce HoloScene, a novel interactive 3D reconstruction framework that simultaneously achieves these requirements. HoloScene leverages a comprehensive interactive scene-graph representation, encoding object geometry, appearance, and physical properties alongside hierarchical and inter-object relationships. Reconstruction is formulated as an energy-based optimization problem, integrating observational data, physical constraints, and generative priors into a unified, coherent objective. Optimization is efficiently performed via a hybrid approach combining sampling-based exploration with gradient-based refinement. The resulting digital twins exhibit complete and precise geometry, physical stability, and realistic rendering from novel viewpoints. Evaluations conducted on multiple benchmark datasets demonstrate superior performance, while practical use-cases in interactive gaming and real-time digital-twin manipulation illustrate HoloScene's broad applicability and effectiveness. Project page: https://xiahongchi.github.io/HoloScene. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05560 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HoloScene: Simulation-Ready Interactive 3D Worlds from a Single Video Xia, Hongchi Lin, Chih-Hao Hsu, Hao-Yu Leboutet, Quentin Gao, Katelyn Paulitsch, Michael Ummenhofer, Benjamin Wang, Shenlong Computer Vision and Pattern Recognition Digitizing the physical world into accurate simulation-ready virtual environments offers significant opportunities in a variety of fields such as augmented and virtual reality, gaming, and robotics. However, current 3D reconstruction and scene-understanding methods commonly fall short in one or more critical aspects, such as geometry completeness, object interactivity, physical plausibility, photorealistic rendering, or realistic physical properties for reliable dynamic simulation. To address these limitations, we introduce HoloScene, a novel interactive 3D reconstruction framework that simultaneously achieves these requirements. HoloScene leverages a comprehensive interactive scene-graph representation, encoding object geometry, appearance, and physical properties alongside hierarchical and inter-object relationships. Reconstruction is formulated as an energy-based optimization problem, integrating observational data, physical constraints, and generative priors into a unified, coherent objective. Optimization is efficiently performed via a hybrid approach combining sampling-based exploration with gradient-based refinement. The resulting digital twins exhibit complete and precise geometry, physical stability, and realistic rendering from novel viewpoints. Evaluations conducted on multiple benchmark datasets demonstrate superior performance, while practical use-cases in interactive gaming and real-time digital-twin manipulation illustrate HoloScene's broad applicability and effectiveness. Project page: https://xiahongchi.github.io/HoloScene. |
| title | HoloScene: Simulation-Ready Interactive 3D Worlds from a Single Video |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.05560 |