Same Pixels, Different Documents: Measuring Part-Level Editability in Image Vectorization

Boheng Zhu, Zehua Wang, Wei Chen, Ming-Hao Lin

Image vectorization is often evaluated by rendering fidelity, which cannot distinguish documents that render identically but support different edits. Edit-based evaluation tests outcomes, but an oracle that gathers a part's fragments can conceal the cost of selecting them. We introduce Pass@k, which tests whether a prescribed edit can succeed using objects selectable within k clicks under a specified interaction model. Eight document diagnostics characterize properties relevant to editing beyond these tasks. We assess structural sensitivity through controlled perturbations, practical relevance through designer operations, and reference dependence through alternative drawings. We evaluate twelve open and commercial methods on 211 references, with 635 edit tasks on 93 of them. In each of four reference sets, including two drawn by designers, 64-88% of oracle-passed tasks on parts composed of multiple shapes fail at Pass@1, against 6-40% for single shapes, pooling counts over configurations. Higher fidelity does not guarantee higher Pass@1, while selected diagnostics are associated with designers' effort in our study.