Beyond Plausibility: Verifiable Fine-Grained Image Editing on Structured Assets
Fine-grained image editing requires more than producing a visually plausible result: an editor must execute the requested attribute change precisely while leaving everything else intact. However, existing benchmarks leave a critical gap between realism and verifiability: benchmarks built on realistic images typically rely on human or vision--language model judgments, while deterministic evaluation has largely focused on synthetic shape canvases, with application-oriented extensions primarily limited to charts. This makes it difficult to determine precisely how much of a requested edit was executed, where unintended changes occurred, and whether small differences between models reflect gen...
| Authors | Muyao Wang, Chen Zhu, Shiqi Yang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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