AI RESEARCH
PROVE: A Perceptual RemOVal cohErence Benchmark for Visual Media
arXiv CS.CV
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ArXi:2605.14534v1 Announce Type: new Evaluating object removal in images and videos remains challenging because the task is inherently one-to-many, yet existing metrics frequently disagree with human perception. Full-reference metrics reward copy-paste behaviors over genuine erasure; no-reference metrics suffer from systematic biases such as favoring blurry results; and global temporal metrics are insensitive to localized artifacts within edited regions.