KlingTeam/VideoGen-RewardBench
🏆 [VideoGen-RewardBench Leaderboard] Introduction 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)… See the full description on the dataset page: https://huggingface.co/datasets/KlingTeam/VideoGen-RewardBench.
| Author / organization | KlingTeam |
|---|---|
| License (as declared) | apache-2.0 |
| Downloads | 893 (as reported by Hugging Face) |
| Likes | 10 |
| Created | 8 February 2025 |
| Last updated at source | 10 February 2025 |
Recent changes
- 16 Oct 2026 Description: 🏆 [VideoGen-RewardBench Leaderboard] Introduction 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-Ev → 🏆 [VideoGen-RewardBench Leaderboard] Introduction 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-Ev
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