  Video generation has witnessed significant advancements, yet evaluating these
models remains a challenge. A comprehensive evaluation benchmark for video
generation is indispensable for two reasons: 1) Existing metrics do not fully
align with human perceptions; 2) An ideal evaluation system should provide
insights to inform future developments of video generation. To this end, we
present VBench, a comprehensive benchmark suite that dissects "video generation
quality" into specific, hierarchical, and disentangled dimensions, each with
tailored prompts and evaluation methods. VBench has three appealing properties:
1) Comprehensive Dimensions: VBench comprises 16 dimensions in video generation
(e.g., subject identity inconsistency, motion smoothness, temporal flickering,
and spatial relationship, etc). The evaluation metrics with fine-grained levels
reveal individual models' strengths and weaknesses. 2) Human Alignment: We also
provide a dataset of human preference annotations to validate our benchmarks'
alignment with human perception, for each evaluation dimension respectively. 3)
Valuable Insights: We look into current models' ability across various
evaluation dimensions, and various content types. We also investigate the gaps
between video and image generation models. We will open-source VBench,
including all prompts, evaluation methods, generated videos, and human
preference annotations, and also include more video generation models in VBench
to drive forward the field of video generation.
