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. To address these limitations, we propose RC (Removal Coherence), a pair of perception-aligned metrics: RC-S, which measures spatial coherence via sliding-window feature comparison between masked and background regions, and RC-T, which measures temporal consistency via distribution tracking within shared restored regions across adjacent frames. To validate RC and support community benchmarking, we further introduce PROVE-Bench, a two-tier real-world benchmark comprising PROVE-M, an 80-video paired dataset with motion augmentation, and PROVE-H, a 100-video challenging subset without ground truth. Together, RC metrics and PROVE-Bench form the PROVE (Perceptual RemOVal cohErence) evaluation framework for visual media. Experiments across diverse image and video benchmarks demonstrate that RC achieves substantially stronger alignment with human judgments than existing evaluation protocols. The code for RC metrics and PROVE-Bench are publicly available at: https://github.com/xiaomi-research/prove/.

翻译：图像和视频中的物体移除评估依然充满挑战，因为该任务本质上是“一对多”的，然而现有评估指标常与人类感知相悖。全参考指标倾向于奖励复制粘贴行为而非真正擦除；无参考指标存在系统性偏差，例如偏好模糊结果；而全局时序指标对编辑区域内局部伪影不敏感。为解决这些局限，我们提出RC（移除一致性），这是一对感知对齐指标：RC-S通过滑动窗口特征比较掩膜区域与背景区域来衡量空间一致性，RC-T通过追踪相邻帧共享修复区域内的分布来衡量时间一致性。为验证RC并支持社区基准测试，我们进一步引入PROVE-Bench，这是一个双层真实世界基准，包含PROVE-M（一个含运动增强的80视频配对数据集）和PROVE-H（一个无真值的100视频挑战子集）。RC指标与PROVE-Bench共同构成面向视觉媒体的PROVE（感知移除一致性）评估框架。在不同图像和视频基准上的实验表明，RC相比现有评估协议实现了与人类判断显著更强的一致性。RC指标与PROVE-Bench的代码已开源：https://github.com/xiaomi-research/prove/。