While current video generation focuses on text or image conditions, practical applications like video editing and vlogging often need to seamlessly connect separate clips. In our work, we introduce Video Connecting, an innovative task that aims to generate smooth intermediate video content between given start and end clips. However, the absence of standardized evaluation benchmarks has hindered the development of this task. To bridge this gap, we proposed 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 VQS, Start-End Consistency Score SECS, and Transition Smoothness Score TSS. Together, they form a comprehensive framework that moves beyond conventional quality-only metrics. We evaluated multiple state-of-the-art video generation models on VC-Bench. Experimental results reveal significant limitations in maintaining start-end consistency and transition smoothness, leading to lower overall coherence and fluidity. We expect that VC-Bench will serve as a pioneering benchmark to inspire and guide future research in video connecting. The evaluation metrics and dataset are publicly available at: https://anonymous.4open.science/r/VC-Bench-1B67/.

翻译：当前视频生成技术主要关注文本或图像条件，然而在视频编辑和视频博客等实际应用中，通常需要将独立片段无缝连接。本文提出视频连接这一创新任务，其目标是在给定的起始与结束片段之间生成平滑的中间视频内容。然而，标准化评估基准的缺失阻碍了该任务的发展。为填补这一空白，我们提出了VC-Bench——一个专为视频连接设计的新型基准测试。该基准包含从公共平台收集的1,579个高质量视频，涵盖15个主类别和72个子类别，以确保多样性与结构性。VC-Bench聚焦三个核心维度：视频质量分数VQS、首尾一致性分数SECS以及过渡平滑度分数TSS。三者共同构成了超越传统单一质量评估的综合框架。我们在VC-Bench上评估了多种前沿视频生成模型。实验结果表明，现有模型在保持首尾一致性与过渡平滑性方面存在显著局限，导致整体连贯性与流畅度不足。我们期待VC-Bench能作为开创性基准，激发并引导未来视频连接领域的研究。评估指标与数据集已公开于：https://anonymous.4open.science/r/VC-Bench-1B67/。