VC-Bench: Pioneering the Video Connecting Benchmark with a Dataset and Evaluation Metrics
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908791043260416 |
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| author | Yin, Zhiyu Liu, Zhipeng Chen, Kehai Liu, Lemao Liu, Jin Li, Hong-Dong Xiang, Yang Zhang, Min |
| author_facet | Yin, Zhiyu Liu, Zhipeng Chen, Kehai Liu, Lemao Liu, Jin Li, Hong-Dong Xiang, Yang Zhang, Min |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19236 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VC-Bench: Pioneering the Video Connecting Benchmark with a Dataset and Evaluation Metrics Yin, Zhiyu Liu, Zhipeng Chen, Kehai Liu, Lemao Liu, Jin Li, Hong-Dong Xiang, Yang Zhang, Min Computer Vision and Pattern Recognition Multimedia 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/. |
| title | VC-Bench: Pioneering the Video Connecting Benchmark with a Dataset and Evaluation Metrics |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2601.19236 |