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

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.

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.

Object removal is ill-posed and one-to-many : many restorations are plausible for the same hole, so no unique ground truth exists.

Both share one idea: local distribution matching in deep feature space instead of global aggregation.

RC-S (spatial) : Connected-component analysis splits the mask into independent targets. Each bounding box is expanded by one third of its side length, the crop goes to DINOv2, and the mask is downsampled to feature resolution. A w×w window slides across the feature map, computing squared Maximum Mean Discrepancy with a Gaussian RBF kernel between masked and local background features. Scores are averaged per target, then across targets.

RC-T (temporal) : Adjacent frames are cropped jointly under the union of their masks to avoid misalignment, then MMD is computed only inside the intersection — the region restored in both frames. An ablation shows that removing the crop makes RC-T blind to injected corruption.

Against human rankings from 20 participants aggregated by Borda count, RC-S reaches 0.59 average Kendall’s τ and 0.66 Spearman’s ρ , versus 0.26/0.29 for ReMOVE and 0.16/0.18 for CFD. It ranks first on five of six benchmarks. Background-only FR variants correlate negatively on most datasets. On RORD-Val, RC-S prefers the clean image over blurred and region-swapped variants in 100% of cases ; ReMOVE manages 60.06% and CFD 49.27% under blur. RC-T responds monotonically to rising corruption where TC and TF do not.