Abstract
The rise of personalized generative models raises a central question: how should we evaluate identity preservation? Given a reference image (e.g., one's pet), we expect the generated image to retain precise details attached to the subject's identity. However, current generative evaluation metrics emphasize the overall semantic similarity between the reference and the output, and overlook these fine-grained discriminative details. We introduce Finer-Personalization Rank, an evaluation protocol tailored to identity preservation. Instead of pairwise similarity, Finer-Personalization Rank adopts a ranking view: it treats each generated image as a query against an identity-labeled gallery consisting of visually similar real images. Retrieval metrics (e.g., mean average precision) measure performance, where higher scores indicate that identity-specific details (e.g., a distinctive head spot) are preserved. We assess identity at multiple granularities -- from fine-grained categories (e.g., bird species, car models) to individual instances (e.g., re-identification). Across CUB, Stanford Cars, and animal Re-ID benchmarks, Finer-Personalization Rank more faithfully reflects identity retention than semantic-only metrics and reveals substantial identity drift in several popular personalization methods. These results position the gallery-based protocol as a principled and practical evaluation for personalized generation.
Abstract (translated)
个性化生成模型的兴起引发了一个核心问题：我们应该如何评估身份保持？给定一个参考图像（例如某人的宠物），我们期望生成的图像是以精确细节保留主体的身份特征。然而，当前的生成评估指标强调了参考图像和输出图像之间的整体语义相似性，并忽略了这些细微的区别性细节。为此，我们引入了一种针对身份保持量化的评估协议——Finer-Personalization Rank。 与传统的成对相似度相比，Finer-Personalization Rank 采用了一个排名视角：它将每个生成的图像视为一个查询，在由视觉上类似的现实图像组成的、包含身份标签的图库中进行检索。通过使用诸如平均精度等检索指标来衡量性能，得分越高表示特定于身份的独特细节（例如独特的头部标记）得以保留。 我们从多个粒度层面上评估了身份——从细微类别（如鸟类种类、汽车型号）到个体实例（如再识别）。在 CUB 数据集、Stanford Cars 和动物 Re-ID 基准测试中，Finer-Personalization Rank 比仅基于语义的指标更真实地反映了身份保持，并且揭示了几个流行的个性化方法中存在的显著的身份漂移现象。 这些结果将基于图库的协议确立为个性化工作者评估的一个原理性和实用性的标准。
URL
https://arxiv.org/abs/2512.19026
