kevinson7515/VC-Bench

We propose VC-Bench, a novel benchmark specifically designed for video connecting. It includes 1,579 high-quality videos collected from public platforms, covering 15 main categories and 72 subcategories to ensure diversity and structure. VC-Bench focuses on three core aspects: Video Quality Score V Q S , Start-End Consistency Score S E C S , and Transition Smoothness Score T S S . Additionally, we employed Qwen2-VL to equip each video with detailed and expressive textual descriptions. We also conduct Human Alignment for the generated videos for each metrics, and show that VC-Bench evaluation results are well aligned with human perceptions. VC-Bench can provide valuable insights from multiple perspectives. VC-Bench supports a wide range of video generation tasks, including Text-to-Video(T2V), Image-to-Video(I2V) and First-Last-Frame-to-Video(FLF2V), with an adaptive Image Suite for fair evaluation across different settings. It evaluates not only technical quality but also the trustworthiness of generative models, offering a comprehensive view of model performance. We expect that VC-Bench will serve as a pioneering benchmark to inspire and guide future research in video connecting.