RISE-Video: Can Video Generators Decode Implicit World Rules?

• URL: http://arxiv.org/abs/2602.05986v1
• Date: Thu, 05 Feb 2026 18:36:10 GMT
• Title: RISE-Video: Can Video Generators Decode Implicit World Rules?
• Authors: Mingxin Liu, Shuran Ma, Shibei Meng, Xiangyu Zhao, Zicheng Zhang, Shaofeng Zhang, Zhihang Zhong, Peixian Chen, Haoyu Cao, Xing Sun, Haodong Duan, Xue Yang,
• Abstract summary: We present RISE-Video, a pioneering reasoning-oriented benchmark for Text-Image-to-Video (TI2V) synthesis.<n>RISE-Video comprises 467 meticulously human-annotated samples spanning eight rigorous categories.<n>We propose an automated pipeline leveraging Large Multimodal Models (LMMs) to emulate human-centric assessment.
• Score: 71.92434352963427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While generative video models have achieved remarkable visual fidelity, their capacity to internalize and reason over implicit world rules remains a critical yet under-explored frontier. To bridge this gap, we present RISE-Video, a pioneering reasoning-oriented benchmark for Text-Image-to-Video (TI2V) synthesis that shifts the evaluative focus from surface-level aesthetics to deep cognitive reasoning. RISE-Video comprises 467 meticulously human-annotated samples spanning eight rigorous categories, providing a structured testbed for probing model intelligence across diverse dimensions, ranging from commonsense and spatial dynamics to specialized subject domains. Our framework introduces a multi-dimensional evaluation protocol consisting of four metrics: \textit{Reasoning Alignment}, \textit{Temporal Consistency}, \textit{Physical Rationality}, and \textit{Visual Quality}. To further support scalable evaluation, we propose an automated pipeline leveraging Large Multimodal Models (LMMs) to emulate human-centric assessment. Extensive experiments on 11 state-of-the-art TI2V models reveal pervasive deficiencies in simulating complex scenarios under implicit constraints, offering critical insights for the advancement of future world-simulating generative models.

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