VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic
  Faithfulness
Authors: name: Dian Zheng; name: Ziqi Huang; name: Hongbo Liu; name: Kai Zou; name: Yinan He; name: Fan Zhang; name: Yuanhan Zhang; name: Jingwen He; name: Wei-Shi Zheng; name: Yu Qiao; name: Ziwei Liu
Published: 2025-03-27 17:57:01

Abstract: 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.