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VIEScore2: Unified Image Evaluation with Spatially Grounded Explanations

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Briefings

VIEScore2 is a new image evaluation system that predicts quality scores and defect locations in a single pass using an N x N grid representation. It was trained on 38K examples across generation and editing tasks and achieved an overall-score SRCC of 0.601 on the primary suite, outperforming Gemini-3-Flash (0.491). The system also outperforms general-purpose VLMs and specialized spatial evaluators on three of six benchmarks for defect localization.

Key points

• VIEScore2 predicts quality scores and defect locations in a single pass using an N x N grid representation.
• It achieved an overall-score SRCC of 0.601 on the primary suite, outperforming Gemini-3-Flash (0.491) on matched inputs.

Context

This research addresses the limitation that existing synthetic image evaluators provide only scalar scores without identifying supporting image regions. VIEScore2 introduces a unified evaluator for image generation and editing tasks with optional conditioning images.

Limitations

The study covers only the primary suite of benchmarks; performance on other datasets beyond training sources remains unverified. Results are based on supervised fine-tuning followed by GRPO optimization, and the parameter-free parser's effectiveness is inferred from its successful conversion of structured predictions into readable explanations.

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