benchmarks/edit/code/VE-Bench/vebench/README.md

Easy Use

VE-Bench can be installed with a single pip command. Since the model employs normalization during training, its output does not represent absolute scores. We recommend performing comparisons between video pairs, as demonstrated below:

pip install vebench

When comparing videos:

from vebench import VEBenchModel

evaluator = VEBenchModel()

score1 = evaluator.evaluate('A black-haired boy is turning his head', 'assets/src.mp4', 'assets/dst.mp4')
score2 = evaluator.evaluate('A black-haired boy is turning his head', 'assets/src.mp4', 'assets/dst2.mp4')
print(score1, score2) # Score1: 1.3563, Score2: 0.66194