PaintBench: Deterministic Evaluation of Precise Visual Editing
While current multimodal models are proficient at open-ended visual editing, executing precise single-answer edits remains an important obstacle. To probe this challenge, we introduce PaintBench, a dynamically scalable benchmark targeting 20 fundamental precise visual editing operations across four categories: geometric transformation, structural manipulation, color change, and symbolic reasoning. Procedural generation with configurable complexity enables an effectively infinite, contamination-resistant evaluation suite, and deterministic pixel-level evaluation eliminates reliance on bias-prone judge models. Across 11 image editing models, we find overall low performance, with the current...
| Authors | Kai Xu, Ellis Brown, Shrikar Madhu et al. |
|---|---|
| Primary category | cs.GR |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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