VideoGen-RewardBench is a comprehensive benchmark designed to evaluate the performance of video reward models on modern text-to-video (T2V) systems. Derived from the third-party VideoGen-Eval (Zeng et.al, 2024), we constructing 26.5k (prompt, Video A, Video B) triplets and employing expert annotators to provide pairwise preference labels.
These annotations are based on key evaluation dimensions—Visual Quality (VQ), Motion… See the full description on the dataset page:
https://huggingface.co/datasets/KlingTeam/VideoGen-RewardBench.
