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Impossible Videos

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arxiv 2503.14378 v1 pith:6GJIZ4AX submitted 2025-03-18 cs.CV

Impossible Videos

Zechen Bai , Hai Ci , Mike Zheng Shou This is my paper
classification cs.CV
keywords videoimpossiblemodelsvideosunderstandinggenerationbenchmarkcomprehensive
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Synthetic videos nowadays is widely used to complement data scarcity and diversity of real-world videos. Current synthetic datasets primarily replicate real-world scenarios, leaving impossible, counterfactual and anti-reality video concepts underexplored. This work aims to answer two questions: 1) Can today's video generation models effectively follow prompts to create impossible video content? 2) Are today's video understanding models good enough for understanding impossible videos? To this end, we introduce IPV-Bench, a novel benchmark designed to evaluate and foster progress in video understanding and generation. IPV-Bench is underpinned by a comprehensive taxonomy, encompassing 4 domains, 14 categories. It features diverse scenes that defy physical, biological, geographical, or social laws. Based on the taxonomy, a prompt suite is constructed to evaluate video generation models, challenging their prompt following and creativity capabilities. In addition, a video benchmark is curated to assess Video-LLMs on their ability of understanding impossible videos, which particularly requires reasoning on temporal dynamics and world knowledge. Comprehensive evaluations reveal limitations and insights for future directions of video models, paving the way for next-generation video models.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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    cs.CV 2026-06 unverdicted novelty 7.0

    Introduces VidPair-Halluc benchmark of 1K background-controlled adversarial video pairs and 11K QA pairs generated via PairFlow pipeline to evaluate hallucination in LVMs.

  2. YoCausal: How Far is Video Generation from World Model? A Causality Perspective

    cs.CV 2026-05 unverdicted novelty 7.0

    YoCausal benchmark shows video diffusion models detect the arrow of time but lack genuine causal understanding relative to humans.

  3. PhyGround: Benchmarking Physical Reasoning in Generative World Models

    cs.CV 2026-05 accept novelty 7.0

    PhyGround is a new benchmark with curated prompts, a 13-law taxonomy, large-scale human annotations, and an open physics-specialized VLM judge for evaluating physical reasoning in generative video models.

  4. VideoASMR-Bench: Can AI-Generated ASMR Videos Fool VLMs and Humans?

    cs.CV 2025-12 unverdicted novelty 7.0

    VideoASMR-Bench shows state-of-the-art VLMs fail to reliably detect AI-generated ASMR videos from real ones, though humans can still identify the fakes relatively easily.

  5. Hallucination of Multimodal Large Language Models: A Survey

    cs.CV 2024-04 accept novelty 5.0

    The survey organizes causes of hallucinations in MLLMs, reviews evaluation benchmarks and metrics, and outlines mitigation approaches plus open questions.