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Table · dataset · 2025

KlingTeam/VideoGen-RewardBench

Listed in Hugging Face Datasets

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-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: huggingface.co/datasets/KlingTeam/VideoGen-RewardBench.

Links

Documentation and papers

Catalogue records · 1

Topics

Stated by source
tabular · text · video
Inferred from text
Video 75%
Provenance · 1 source records, 12 field assertions
SourceKeyLast seenRaw
Hugging Face DatasetsKlingTeam/VideoGen-RewardBench13 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · Hugging Faceconnector:huggingface@1.0.0/gated
concepts[field].local:field:computer-science-aimapping · Hugging Faceconnector:huggingface@1.0.0
concepts[modality].hf_modality:tabularsource · Hugging Faceconnector:huggingface@1.0.0
concepts[modality].hf_modality:textsource · Hugging Faceconnector:huggingface@1.0.0
concepts[modality].hf_modality:videosource · Hugging Faceconnector:huggingface@1.0.0
concepts[modality].local:modality:videoenrichment · Hugging Facekeyword-concept-rules@1.0.0title+description (75%)
created_datesource · Hugging Faceconnector:huggingface@1.0.0
descriptionsource · Hugging Faceconnector:huggingface@1.0.0/description
licensesource · Hugging Faceconnector:huggingface@1.0.0/tags[license:*]
publication_datesource · Hugging Faceconnector:huggingface@1.0.0
titlesource · Hugging Faceconnector:huggingface@1.0.0/id
updated_datesource · Hugging Faceconnector:huggingface@1.0.0