Title: VC-Bench: Pioneering the Video Connecting Benchmark with a Dataset and Evaluation Metrics
Primary area: datasets and benchmarks
Keywords: Video Generation, Video Connecting, VC-Bench

Abstract: Current video generation techniques mainly focus on creating under text or image conditioning. However, real-world applications often require seamlessly connecting two independent video clips. To address this, 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, with notable performance gaps among models. 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/.