VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908496066248704 |
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| author | Zheng, Dian Huang, Ziqi Liu, Hongbo Zou, Kai He, Yinan Zhang, Fan Gu, Lulu Zhang, Yuanhan He, Jingwen Zheng, Wei-Shi Qiao, Yu Liu, Ziwei |
| author_facet | Zheng, Dian Huang, Ziqi Liu, Hongbo Zou, Kai He, Yinan Zhang, Fan Gu, Lulu Zhang, Yuanhan He, Jingwen Zheng, Wei-Shi Qiao, Yu Liu, Ziwei |
| contents | Video generation has advanced significantly, evolving from producing unrealistic outputs to generating videos that appear visually convincing and temporally coherent. To evaluate these video generative models, benchmarks such as VBench have been developed to assess their faithfulness, measuring factors like per-frame aesthetics, temporal consistency, and basic prompt adherence. However, these aspects mainly represent superficial faithfulness, which focus on whether the video appears visually convincing rather than whether it adheres to real-world principles. While recent models perform increasingly well on these metrics, they still struggle to generate videos that are not just visually plausible but fundamentally realistic. To achieve real "world models" through video generation, the next frontier lies in intrinsic faithfulness to ensure that generated videos adhere to physical laws, commonsense reasoning, anatomical correctness, and compositional integrity. Achieving this level of realism is essential for applications such as AI-assisted filmmaking and simulated world modeling. To bridge this gap, we introduce VBench-2.0, a next-generation benchmark designed to automatically evaluate video generative models for their intrinsic faithfulness. VBench-2.0 assesses five key dimensions: Human Fidelity, Controllability, Creativity, Physics, and Commonsense, each further broken down into fine-grained capabilities. Tailored to individual dimensions, our evaluation framework integrates generalists such as SOTA VLMs and LLMs, and specialists, including anomaly detection methods proposed for video generation. We conduct extensive human annotations to ensure evaluation alignment with human judgment. By pushing beyond superficial faithfulness toward intrinsic faithfulness, VBench-2.0 aims to set a new standard for the next generation of video generative models in pursuit of intrinsic faithfulness. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_21755 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness Zheng, Dian Huang, Ziqi Liu, Hongbo Zou, Kai He, Yinan Zhang, Fan Gu, Lulu Zhang, Yuanhan He, Jingwen Zheng, Wei-Shi Qiao, Yu Liu, Ziwei Computer Vision and Pattern Recognition Video generation has advanced significantly, evolving from producing unrealistic outputs to generating videos that appear visually convincing and temporally coherent. To evaluate these video generative models, benchmarks such as VBench have been developed to assess their faithfulness, measuring factors like per-frame aesthetics, temporal consistency, and basic prompt adherence. However, these aspects mainly represent superficial faithfulness, which focus on whether the video appears visually convincing rather than whether it adheres to real-world principles. While recent models perform increasingly well on these metrics, they still struggle to generate videos that are not just visually plausible but fundamentally realistic. To achieve real "world models" through video generation, the next frontier lies in intrinsic faithfulness to ensure that generated videos adhere to physical laws, commonsense reasoning, anatomical correctness, and compositional integrity. Achieving this level of realism is essential for applications such as AI-assisted filmmaking and simulated world modeling. To bridge this gap, we introduce VBench-2.0, a next-generation benchmark designed to automatically evaluate video generative models for their intrinsic faithfulness. VBench-2.0 assesses five key dimensions: Human Fidelity, Controllability, Creativity, Physics, and Commonsense, each further broken down into fine-grained capabilities. Tailored to individual dimensions, our evaluation framework integrates generalists such as SOTA VLMs and LLMs, and specialists, including anomaly detection methods proposed for video generation. We conduct extensive human annotations to ensure evaluation alignment with human judgment. By pushing beyond superficial faithfulness toward intrinsic faithfulness, VBench-2.0 aims to set a new standard for the next generation of video generative models in pursuit of intrinsic faithfulness. |
| title | VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.21755 |