VC-Bench: Pioneering the Video Connecting Benchmark with a Dataset and Evaluation Metrics

2026-01-27

AI Summary

This work addresses the lack of systematic evaluation in existing video generation methods regarding start-end consistency and transition smoothness. It introduces, for the first time, the “video stitching” task, which aims to generate coherent intermediate content between given start and end video clips. To support this task, the authors construct VC-Bench, a high-quality benchmark dataset comprising 1,579 videos spanning 15 major categories and 72 subcategories. A multidimensional evaluation framework is proposed, featuring three novel metrics: Video Quality Score (VQS), Start-End Consistency Score (SECS), and Transition Smoothness Score (TSS). Comprehensive evaluations of state-of-the-art models reveal significant deficiencies in temporal coherence and motion smoothness, thereby highlighting critical directions for future research.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Natural Language Processing: Generation
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data generation and simulation
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

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/.

Problem

• Video Connecting
• Video Generation
• Evaluation Benchmark
• Seamless Transition
• Start-End Consistency

Innovation

• Video Connecting
• VC-Bench
• Transition Smoothness
• Start-End Consistency
• Video Generation Benchmark