Researchers Introduce Benchmark and Dataset for Video AI That Corrects Cooking Mistakes in Real Time
Researchers have introduced CoVEBench, a new benchmark designed to evaluate how well AI video editing models handle compositional, multi-step editing instructions. The benchmark includes 416 source videos, 626 multi-point editing instructions, and 9,990 fine-grained checklist items spanning diverse editing dimensions. The work highlights a significant gap between current model capabilities and real-world user needs, as models frequently omit edits, violate preservation constraints, or introduce visual artifacts.
A team of researchers has released CoVEBench, a compositional video editing benchmark aimed at diagnosing the limitations of current text-guided video editing models when faced with complex, real-world instructions. While existing models perform reasonably well on isolated tasks such as style transfer or object insertion, they struggle when a single prompt requires multiple coupled edits—such as simultaneously changing a subject, an action, and a camera angle while preserving unrelated content. CoVEBench addresses shortcomings in prior benchmarks, which relied on isolated edits and coarse global metrics that obscure model failures in compositional scenarios. The benchmark evaluates models using multimodal large language model (MLLM)-judged instruction compliance and video fidelity, supplemented by automated video quality metrics. Experiments conducted across current state-of-the-art models consistently show that compositional editing remains a profound unsolved challenge, with models frequently failing to execute all requested edits or introducing unwanted artifacts. The authors position CoVEBench as a diagnostic testbed intended to drive progress toward video editing systems capable of handling realistic user workflows.
What different sources said
- arXiv cs.AICenter
CoVEBench: Can Video Editing Models Handle Complex Instructions?
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