Unpainting: Characterizing Brushstroke Order Recovery from Finished Paintings

When a painting is finished, the order in which its strokes were laid down is no longer recorded; only the final surface survives. We ask how much of that hidden order a painting can still reveal. We call this unpainting: recovering the stroke sequence behind a finished canvas. We introduce Pentimento, a conditional diffusion model, and SynBrush, the synthetic corpus of known-order paintings we train it on; rather than guessing one answer, it proposes many plausible orderings and reports how much they agree, with pairwise probabilities well calibrated on held-out paintings. Recovery is real but limited: far better than chance, yet well short of certainty. Targeted interventions trace it predominantly to the learned process prior: hand the model a stranger's canvas, or none at all, and it does just as well; a model trained without a canvas reaches Kendall's $\tau =0.257$ against the conditional model's $0.272$. Meanwhile, a simple rule asking only which stroke covers which recovers twice as much of the order, though it is handed the strokes the model must find. Conditioning closes 5% of that gap: it says substantially more about the stroke parameters than about their order. The finished canvas remembers more about how it was made than the model takes from it. What Pentimento does offer is calibrated confidence in its own samples: answer only the fifth it is surest about and it is right 97% of the time. That confidence is prior-driven too, barely better than a stroke-width heuristic, uncorrelated with what a given canvas determines. We offer unpainting as a controlled study of prior dominance in amortized inverse inference, not a tool for art historians.