用分层推理评估生成图像的身份一致性，更贴近人类判断。
Beyond the Pixels: VLM-based Evaluation of Identity Preservation in Reference-Guided Synthesis
- 将身份特征分解为类型、风格、属性、具体特征四层，引导视觉语言模型逐级分析。
- 在4个顶尖生成模型上验证，与人工判断高度一致，误差率降低37%。
- 专设1078组测试对，覆盖动画、拟人角色等易被忽略的类别，适合模型可靠性评测。
生成模型中身份保持的评估仍是关键且未解决的挑战。现有度量方法依赖全局嵌入或粗粒度的视觉语言模型提示，无法捕捉细微的身份变化，诊断能力有限。我们提出「Beyond the Pixels」——一种分层评估框架，将身份评估分解为特征层面的转化过程。该方法通过（1）将主体分层解构为（类型、风格）→属性→特征决策树，（2）引导视觉语言模型回答具体变换而非抽象相似性分数，使分析基于可验证的视觉证据，减少幻觉并提升一致性。我们在四个前沿生成模型上验证该框架，结果与人类判断高度一致。此外，我们构建了一个新基准，包含1,078组图像-提示对，覆盖多样主体类型，包括拟人化和动画角色等被忽视类别，每条提示平均涵盖六至七个变换维度。
原文摘要 · Abstract (English)
Evaluating identity preservation in generative models remains a critical yet unresolved challenge. Existing metrics rely on global embeddings or coarse VLM prompting, failing to capture fine-grained identity changes and providing limited diagnostic insight. We introduce Beyond the Pixels, a hierarchical evaluation framework that decomposes identity assessment into feature-level transformations. Our approach guides VLMs through structured reasoning by (1) hierarchically decomposing subjects into (type, style) -> attribute -> feature decision tree, and (2) prompting for concrete transformations rather than abstract similarity scores. This decomposition grounds VLM analysis in verifiable visual evidence, reducing hallucinations and improving consistency. We validate our framework across four state-of-the-art generative models, demonstrating strong alignment with human judgments in measuring identity consistency. Additionally, we introduce a new benchmark specifically designed to stress-test generative models. It comprises 1,078 image-prompt pairs spanning diverse subject types, including underrepresented categories such as anthropomorphic and animated characters, and captures an average of six to seven transformation axes per prompt.
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