Xiaomi’s MiLM Plus Releases PROVE: Perception-Aligned Object Removal Metrics RC-S and RC-T With a Real-World Video Benchmark

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Object removal models have improved faster than the metrics used to judge them. Diffusion erasers now reconstruct shadows, reflections and occluded structure convincingly, yet PSNR, SSIM, LPIPS, ReMOVE and CFD frequently rank their outputs the wrong way. The root cause is structural: erasure is an ill-posed, one-to-many task, so no single ground truth exists to compare against. A team from MiLM Plus, Xiaomi Inc. has released PROVE (Perceptual RemOVal cohErence), accepted at ACM MM 2026, to close that gap. PROVE pairs two perception-aligned metrics — RC-S for spatial coherence and RC-T for temporal consistency — with PROVE-Bench, a two-tier real-world video benchmark. Both metrics score the edited region locally, using sliding-window Maximum Mean Discrepancy over DINOv2 features, and neither requires a reference video.

Is it deployable?

Yes — as an evaluation harness, not a product feature. PROVE ships as an Apache 2.0 PyTorch repo with one CLI entry point (run_prove_metrics.py). It needs Python 3.10+, PyTorch 2.6+, Transformers 4.51+, and DINOv2-giant weights. Masks are mandatory; white pixels mark the removed object.

Company level: Any team with one GPU and a mask pipeline. RC-S runs at 134.6 ms/frame on a single RTX 4090, so nightly CI gates are practical for startups; enterprises with large editing catalogs gain most, since no paired ground truth is needed.

Industries: Smartphone gallery and camera apps, short-video editing, e-commerce catalog cleanup, advertising and stock media, film/VFX post-production, real estate imagery, and privacy redaction in mapping …

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