title: VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic
    Faithfulness
summary: 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
  for individual dimensions, our evaluation framework integrates generalists such
  as state-of-the-art VLMs and LLMs, and specialists, including anomaly detection
  methods proposed for video generation. We conduct extensive annotations to
  ensure 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.
paper_authors._id: 67e60823284844fd3014f62b
paper_authors.name: Dian Zheng
paper_authors._id: 67e60823284844fd3014f62c
paper_authors.name: Ziqi Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T08:48:07.968Z
paper_authors._id: 60efe7fa0d920bc7805cada5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/60efe7fa0d920bc7805cada5/2LBrJBjSCOP5ilZIpWLHl.png
paper_authors.fullname: Ziqi Huang
paper_authors.type: user
paper_authors.user: Ziqi
paper_authors._id: 67e60823284844fd3014f62d
paper_authors.name: Hongbo Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T08:48:14.875Z
paper_authors._id: 6690dfd73bbfdee5f43ffc4d
paper_authors.avatarUrl: /avatars/88ff9b61663299d7751037696a75f1d7.svg
paper_authors.fullname: Hongbo Liu
paper_authors.type: user
paper_authors.user: HongboLiu
paper_authors._id: 67e60823284844fd3014f62e
paper_authors.name: Kai Zou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-28T08:37:25.760Z
paper_authors._id: 647993d9f966f086918da59e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/647993d9f966f086918da59e/NDxz3PEpo3srZQNhwT7Qf.jpeg
paper_authors.fullname: kzou
paper_authors.type: user
paper_authors.user: jackyhate
paper_authors._id: 67e60823284844fd3014f62f
paper_authors.name: Yinan He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T08:48:21.118Z
paper_authors._id: 65b9d9961fe588f824fde191
paper_authors.avatarUrl: /avatars/a9245958cc998a4b4b870bf2490fdaee.svg
paper_authors.fullname: Yinan He
paper_authors.type: user
paper_authors.user: yinanhe
paper_authors._id: 67e60823284844fd3014f630
paper_authors.name: Fan Zhang
paper_authors._id: 67e60823284844fd3014f631
paper_authors.name: Yuanhan Zhang
paper_authors._id: 67e60823284844fd3014f632
paper_authors.name: Jingwen He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T08:48:30.978Z
paper_authors._id: 670749a9d827da9f37508209
paper_authors.avatarUrl: /avatars/f14fc05ad405f3967b9af0bcc73d4207.svg
paper_authors.fullname: he jingwen
paper_authors.type: user
paper_authors.user: mimihe
paper_authors._id: 67e60823284844fd3014f633
paper_authors.name: Wei-Shi Zheng
paper_authors._id: 67e60823284844fd3014f634
paper_authors.name: Yu Qiao
paper_authors._id: 67e60823284844fd3014f635
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T08:48:49.191Z
paper_authors._id: 62ab1ac1d48b4d8b048a3473
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
paper_authors.fullname: Ziwei Liu
paper_authors.type: user
paper_authors.user: liuziwei7
paper_title: VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic
    Faithfulness
paper_summary: 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
  for individual dimensions, our evaluation framework integrates generalists such
  as state-of-the-art VLMs and LLMs, and specialists, including anomaly detection
  methods proposed for video generation. We conduct extensive annotations to
  ensure 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.
paper_ai_summary: VBench-2.0 introduces a benchmark for video generation that evaluates intrinsic faithfulness by assessing human fidelity, controllability, creativity, physics, and commonsense.
paper_ai_keywords: VLMs LLMs anomaly detection VBench-2.0