T2VEval: T2V-generated Videos Benchmark Dataset and Objective Evaluation Method

• URL: http://arxiv.org/abs/2501.08545v4
• Date: Tue, 18 Feb 2025 12:58:49 GMT
• Title: T2VEval: T2V-generated Videos Benchmark Dataset and Objective Evaluation Method
• Authors: Zelu Qi, Ping Shi, Shuqi Wang, Zhaoyang Zhang, Fei Zhao, Zefeng Ying, Da Pan,
• Abstract summary: T2VEval is a multi-branch fusion scheme for text-to-video quality evaluation. It assesses videos across three branches: text-video consistency, realness, and technical quality. T2VEval achieves state-of-the-art performance across multiple metrics.
• Score: 13.924105106722534
• License:
• 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.

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