SceneScribe-1M: A Large-Scale Video Dataset with Comprehensive Geometric and Semantic Annotations
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910165213642752 |
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| author | Wang, Yunnan Zheng, Kecheng Wang, Jianyuan Chen, Minghao Novotny, David Rupprecht, Christian Xu, Yinghao Zhu, Xing Zeng, Wenjun Jin, Xin Shen, Yujun |
| author_facet | Wang, Yunnan Zheng, Kecheng Wang, Jianyuan Chen, Minghao Novotny, David Rupprecht, Christian Xu, Yinghao Zhu, Xing Zeng, Wenjun Jin, Xin Shen, Yujun |
| contents | The convergence of 3D geometric perception and video synthesis has created an unprecedented demand for large-scale video data that is rich in both semantic and spatio-temporal information. While existing datasets have advanced either 3D understanding or video generation, a significant gap remains in providing a unified resource that supports both domains at scale. To bridge this chasm, we introduce SceneScribe-1M, a new large-scale, multi-modal video dataset. It comprises one million in-the-wild videos, each meticulously annotated with detailed textual descriptions, precise camera parameters, dense depth maps, and consistent 3D point tracks. We demonstrate the versatility and value of SceneScribe-1M by establishing benchmarks across a wide array of downstream tasks, including monocular depth estimation, scene reconstruction, and dynamic point tracking, as well as generative tasks such as text-to-video synthesis, with or without camera control. By open-sourcing SceneScribe-1M, we aim to provide a comprehensive benchmark and a catalyst for research, fostering the development of models that can both perceive the dynamic 3D world and generate controllable, realistic video content. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_07990 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SceneScribe-1M: A Large-Scale Video Dataset with Comprehensive Geometric and Semantic Annotations Wang, Yunnan Zheng, Kecheng Wang, Jianyuan Chen, Minghao Novotny, David Rupprecht, Christian Xu, Yinghao Zhu, Xing Zeng, Wenjun Jin, Xin Shen, Yujun Computer Vision and Pattern Recognition The convergence of 3D geometric perception and video synthesis has created an unprecedented demand for large-scale video data that is rich in both semantic and spatio-temporal information. While existing datasets have advanced either 3D understanding or video generation, a significant gap remains in providing a unified resource that supports both domains at scale. To bridge this chasm, we introduce SceneScribe-1M, a new large-scale, multi-modal video dataset. It comprises one million in-the-wild videos, each meticulously annotated with detailed textual descriptions, precise camera parameters, dense depth maps, and consistent 3D point tracks. We demonstrate the versatility and value of SceneScribe-1M by establishing benchmarks across a wide array of downstream tasks, including monocular depth estimation, scene reconstruction, and dynamic point tracking, as well as generative tasks such as text-to-video synthesis, with or without camera control. By open-sourcing SceneScribe-1M, we aim to provide a comprehensive benchmark and a catalyst for research, fostering the development of models that can both perceive the dynamic 3D world and generate controllable, realistic video content. |
| title | SceneScribe-1M: A Large-Scale Video Dataset with Comprehensive Geometric and Semantic Annotations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.07990 |