Author

Yu Liu

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Preprint Sep 2026

OmniVBench: A Benchmark and Large-Scale Dataset for Omni Reference-to-Video Generation

Omni reference-to-video (R2V) generation is emerging as a general paradigm that enables flexible video customization from diverse and compositional visual references. However, existing benchmarks fall short of these emerging capabilities: their test cases cover only a limited range of reference types and compositions,...

Wen-Xue Li, Pei-Yan Guan, Hao-Yan Jiang et al. · 1 citation

Preprint Aug 2026

CoinVE-200K: A Large-Scale High-Quality Dataset for Compositional Instruction-Guided Video Editing

The quality and diversity of instruction-based video editing datasets are steadily improving, yet existing datasets mainly focus on single editing operations and fall short in supporting compositional instruction-guided video editing. In particular, multiple editing intents must be jointly understood and faithfully exe...

Fuchen Long, Cong Wang, Zitao Gao et al. · 1 citation

Preprint Aug 2026

OmniEdit-Bench: A Comprehensive Benchmark for Instruction-based Video Editing

The OmniEdit-Bench provides a comprehensive and reliable testbed for evaluating instruction-based video editing and offers insights into future research directions, including an accuracy-aware penalty mechanism that conditions other scores on accuracy, preventing visually plausible but incorrect edits from receiving in...

Chenxuan Miao, Yutong Feng, Yi Lu et al. · 1 citation