# T2VEval: Benchmark Dataset and Objective Evaluation Method for T2V-generated Videos Source: [T2VEval: Benchmark Dataset and Objective Evaluation Method for T2V-generated Videos]() Collection index: [llms.txt]() Data updated: 2026-01-28T05:00:19.604Z Authors: Zelu Qi, Ping Shi, Shuqi Wang, Chaoyang Zhang, Fei Zhao, Zefeng Ying, Da Pan, Xi Yang, Zheqi He, Teng Dai Published: 2025-01-15T03:11:33.000Z ## Abstract Recent advances in text-to-video (T2V) technology, as demonstrated by models such as Runway Gen-3, Pika, Sora, and Kling, have significantly broadened the applicability and popularity of the technology. This progress has created a growing demand for accurate quality assessment metrics to evaluate the perceptual quality of T2V-generated videos and optimize video generation models. However, assessing the quality of text-to-video outputs remain challenging due to the presence of highly complex distortions, such as unnatural actions and phenomena that defy human cognition. To address these challenges, we constructed T2VEval-Bench, a multi-dimensional benchmark dataset for text-to-video quality evaluation, which contains 148 textual prompts and 1,783 videos generated by 13 T2V models. To ensure a comprehensive evaluation, we scored each video on four dimensions in the subjective experiment, which are overall impression, text-video consistency, realness, and technical quality. Based on T2VEval-Bench, we developed T2VEval, a multi-branch fusion scheme for T2V quality evaluation. T2VEval assesses videos across three branches: text-video consistency, realness, and technical quality. Using an attention-based fusion module, T2VEval effectively integrates features from each branch and predicts scores with the aid of a large language model. Additionally, we implemented a divide-and-conquer training strategy, enabling each branch to learn targeted knowledge while maintaining synergy with the others. Experimental results demonstrate that T2VEval achieves state-of-the-art performance across multiple metrics. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [AIGC Video Quality Assessment on T2VQA-DB (test)](): AIGC Video Quality Assessment; T2VQA-DB (test); SRCC: 0.805. ## Original sources and implementation [Original paper]() [PDF]()