SCP: Spatial Causal Prediction in Video
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913003606114304 |
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| author | Zhao, Yanguang Yang, Jie Wu, Shengqiong Hu, Shutong Qiu, Hongbo Wang, Yu Zhang, Guijia Ze, Tan Kai Fei, Hao Lin, Chia-Wen Lee, Mong-Li Hsu, Wynne |
| author_facet | Zhao, Yanguang Yang, Jie Wu, Shengqiong Hu, Shutong Qiu, Hongbo Wang, Yu Zhang, Guijia Ze, Tan Kai Fei, Hao Lin, Chia-Wen Lee, Mong-Li Hsu, Wynne |
| contents | Spatial reasoning, the ability to understand spatial relations, causality, and dynamic evolution, is central to human intelligence and essential for real-world applications such as autonomous driving and robotics. Existing studies, however, primarily assess models on visible spatio-temporal understanding, overlooking their ability to infer unseen past or future spatial states. In this work, we introduce Spatial Causal Prediction (SCP), a new task paradigm that challenges models to reason beyond observation and predict spatial causal outcomes. We further construct SCP-Bench, a benchmark comprising 2,500 QA pairs across 1,181 videos spanning diverse viewpoints, scenes, and causal directions, to support systematic evaluation. Through comprehensive experiments on {23} state-of-the-art models, we reveal substantial gaps between human and model performance, limited temporal extrapolation, and weak causal grounding. We further analyze key factors influencing performance and propose perception-enhancement and reasoning-guided strategies toward advancing spatial causal intelligence. The project page is https://guangstrip.github.io/SCP-Bench. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03944 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SCP: Spatial Causal Prediction in Video Zhao, Yanguang Yang, Jie Wu, Shengqiong Hu, Shutong Qiu, Hongbo Wang, Yu Zhang, Guijia Ze, Tan Kai Fei, Hao Lin, Chia-Wen Lee, Mong-Li Hsu, Wynne Computer Vision and Pattern Recognition Spatial reasoning, the ability to understand spatial relations, causality, and dynamic evolution, is central to human intelligence and essential for real-world applications such as autonomous driving and robotics. Existing studies, however, primarily assess models on visible spatio-temporal understanding, overlooking their ability to infer unseen past or future spatial states. In this work, we introduce Spatial Causal Prediction (SCP), a new task paradigm that challenges models to reason beyond observation and predict spatial causal outcomes. We further construct SCP-Bench, a benchmark comprising 2,500 QA pairs across 1,181 videos spanning diverse viewpoints, scenes, and causal directions, to support systematic evaluation. Through comprehensive experiments on {23} state-of-the-art models, we reveal substantial gaps between human and model performance, limited temporal extrapolation, and weak causal grounding. We further analyze key factors influencing performance and propose perception-enhancement and reasoning-guided strategies toward advancing spatial causal intelligence. The project page is https://guangstrip.github.io/SCP-Bench. |
| title | SCP: Spatial Causal Prediction in Video |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.03944 |