Evaluating AI Video Generators with VBench Quiz

Answers and explanations appear as you take the quiz.

1. How many evaluation dimensions does the original VBench define?
   • 4
   • 8
   • 16
   • 32
2. Which dimension would reveal that a model produces nearly static videos?
   • Dynamic degree
   • Background consistency
   • Aesthetic quality
   • Color
3. What does VBench use to measure subject consistency across frames?
   • Pixel histograms
   • FID statistics
   • Audio embeddings
   • DINO features
4. Which two families group VBench's dimensions?
   • Speed and cost
   • Video quality and video-condition consistency
   • Realism and diversity
   • Training and inference
5. Why can reading only VBench's total score be misleading?
   • The total is always zero for open models
   • It measures only audio
   • A timid, nearly frozen model can score high on consistency while barely moving
   • It ignores every dimension except color
6. How is motion smoothness estimated?
   • By checking how well a frame interpolation model reconstructs dropped frames
   • By counting frames per second
   • By asking users to rate each clip
   • By measuring file size
7. What does VBench-2.0 focus on?
   • Resolution only
   • Deeper properties such as physics, commonsense and human anatomy
   • Audio synchronization only
   • Replacing all evaluators with FID
8. Why do human video arenas still matter?
   • They are cheaper than automatic metrics
   • They are perfectly reproducible
   • They measure only frame sharpness
   • They capture overall preference, including qualities such as plausible motion that evaluators miss