METICULOUS MAGNIFIER: EVALUATING FINE- GRAINED SUBJECT-DETAIL PRESERVATION IN SUBJECT-DRIVEN GENERATION

Subject-driven generation (SDG) achieves strong high-level semantic consistency, yet preserving and evaluating localized details such as text and logos remains challenging. We study this gap by introducing DreamBench-Detail a benchmark of detail-rich product subjects, and a controlled protocol that induces ordered levels of localized detail degradation while holding generation settings fixed. Under this protocol, embedding-based metrics show weak score separation, while holistic MLLM evaluators assign tied scores to approximately 75% of comparisons. When contextual variation is introduced, the evaluated metrics either remain weak or lose ranking accuracy. To address these limitations, we propose Meticulous Magnifier, which identifies reference-specific salient targets, localizes them in both images, and aggregates rubric-guided crop-level judgments. Across multiple distortion levels and SDG models, Meticulous Magnifier achieves 78.54% of Pairwise Accuracy with a 6.69% tie rate. It retains 75.33% accuracy under additional contextual variation, substantially outperforming the evaluated baselines.