FoMo turns the forking moment of a diffusion trajectory into automatic perceptual-distance labels, reaching 0.733 SROCC on PIPAL and beating metrics trained on human-annotated data
The work proposes FoMo: forward-noising a reference image to a sampled timestep and then denoising it independently, using that forking moment as a pointwise perceptual-distance label, so training data can be generated with no human annotation, and trains reference-based IQA metrics with a RankNet-style global ranking objective; across four benchmarks and seven backbones it achieves the best average performance, with LPIPS-Alex reaching 0.733 SROCC on PIPAL versus 0.577 for the same architecture trained on human-annotated KADID-10k labels.