Evaluation-Verification Reward for Consistent Multi-Reference Image Editing
While recent image editing models have made rapid progress, multi-reference editing remains challenging, particularly in maintaining visual consistency across references and ensuring overall visual harmony. Reinforcement learning has proven highly effective for text-to-image generation and single-image editing, but its extension to multi-reference editing is hindered by the absence of suitable reward models that capture multi-image relational constraints. Moreover, naively using multimodal large language models(MLLMs) as zero-shot evaluators faces a key tension between hallucination-prone long-form reasoning and the limited deductive power of short-form judgments. We address these issues ...
| Authors | Yingmao Miao, Pengfei Zhang, Xiaochen Lv et al. |
|---|---|
| Primary category | cs.CV |
| Published | 31 July 2026 |
| Last updated at source | 5 August 2026 |
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