RISE-Video: Can Video Generators Decode Implicit World Rules?
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: Reasoning Alignment, Temporal Consistency, Physical Rationality, and 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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Mingxin Liu
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Shuran Ma
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Shibei Meng
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Xiangyu Zhao
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Zicheng Zhang
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Shaofeng Zhang
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Zhihang Zhong
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2026-03-10T18:02:39.635Z
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Zhihang Zhong
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Peixian Chen
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Haoyu Cao
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2026-04-08T11:40:40.344Z
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caohaoyu
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Xing Sun
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2026-04-03T09:09:39.101Z
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XING SUN
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Haodong Duan
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Xue Yang
RISE-Video: Can Video Generators Decode Implicit World Rules?
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: Reasoning Alignment, Temporal Consistency, Physical Rationality, and 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.
RISE-Video presents a novel benchmark for evaluating text-image-to-video synthesis models based on cognitive reasoning rather than visual fidelity, using a multi-dimensional metric system and automated LMM-based evaluation.
Text-Image-to-Video multimodal models reasoning alignment temporal consistency physical rationality visual quality large multimodal models automated evaluation