VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic
  Faithfulness
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.
67e60823284844fd3014f62b
Dian Zheng
67e60823284844fd3014f62c
Ziqi Huang
admin_assigned
2025-03-28T08:48:07.968Z
60efe7fa0d920bc7805cada5
https://cdn-avatars.huggingface.co/v1/production/uploads/60efe7fa0d920bc7805cada5/2LBrJBjSCOP5ilZIpWLHl.png
Ziqi Huang
user
Ziqi
67e60823284844fd3014f62d
Hongbo Liu
admin_assigned
2025-03-28T08:48:14.875Z
6690dfd73bbfdee5f43ffc4d
/avatars/88ff9b61663299d7751037696a75f1d7.svg
Hongbo Liu
user
HongboLiu
67e60823284844fd3014f62e
Kai Zou
claimed_verified
2025-03-28T08:37:25.760Z
647993d9f966f086918da59e
https://cdn-avatars.huggingface.co/v1/production/uploads/647993d9f966f086918da59e/NDxz3PEpo3srZQNhwT7Qf.jpeg
kzou
user
jackyhate
67e60823284844fd3014f62f
Yinan He
admin_assigned
2025-03-28T08:48:21.118Z
65b9d9961fe588f824fde191
/avatars/a9245958cc998a4b4b870bf2490fdaee.svg
Yinan He
user
yinanhe
67e60823284844fd3014f630
Fan Zhang
67e60823284844fd3014f631
Yuanhan Zhang
67e60823284844fd3014f632
Jingwen He
admin_assigned
2025-03-28T08:48:30.978Z
670749a9d827da9f37508209
/avatars/f14fc05ad405f3967b9af0bcc73d4207.svg
he jingwen
user
mimihe
67e60823284844fd3014f633
Wei-Shi Zheng
67e60823284844fd3014f634
Yu Qiao
67e60823284844fd3014f635
Ziwei Liu
admin_assigned
2025-03-28T08:48:49.191Z
62ab1ac1d48b4d8b048a3473
https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
Ziwei Liu
user
liuziwei7
VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic
  Faithfulness
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.
VBench-2.0 introduces a benchmark for video generation that evaluates intrinsic faithfulness by assessing human fidelity, controllability, creativity, physics, and commonsense.
VLMs LLMs anomaly detection VBench-2.0