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arXiv · 2510.26412

LoCoT2V-Bench: Benchmarking Long-Form and Complex Text-to-Video Generation

Xiangqing Zheng·Chengyue Wu·Kehai Chen·Min Zhang
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Abstract

Recent advances in text-to-video generation have achieved impressive performance on short clips, yet evaluating long-form generation under complex textual inputs remains a significant challenge. In response to this challenge, we present LoCoT2V-Bench, a benchmark for long video generation (LVG) featuring multi-scene prompts with hierarchical metadata (e.g., character settings and camera behaviors), constructed from collected real-world videos. We further propose LoCoT2V-Eval, a multi-dimensional framework covering perceptual quality, text-video alignment, temporal quality, dynamic quality, and Human Expectation Realization Degree (HERD), with an emphasis on aspects such as fine-grained text-video alignment and temporal character consistency. Experiments on 17 representative LVG models reveal pronounced capability disparities across evaluation dimensions, with strong perceptual quality and background consistency but markedly weaker fine-grained text-video alignment and character consistency. These findings suggest that improving prompt faithfulness and identity preservation remains a key challenge for long-form video generation. Our code and data are released at https://github.com/XqZeppelinhead0702/LoCoT2V-Bench

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Xiangqing Zheng, Chengyue Wu, Kehai Chen, Min Zhang. 2025-10-30. LoCoT2V-Bench: Benchmarking Long-Form and Complex Text-to-Video Generation. https://arxiv.org/abs/2510.26412

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