视觉个性化图灵测试

摘要

我们提出视觉个性化图灵测试（VPTT），这是一种基于感知不可区分性（而非身份复现）来评估情境化视觉个性化的新范式。当模型生成的图像、视频、3D资产等内容，在人类或经过校准的视觉语言模型评判下，与特定人物可能创作或分享的内容无法区分时，即视为通过VPTT。为实现该测试，我们构建了VPTT框架，包含万人角色基准数据集（VPTT-Bench）、视觉检索增强生成器（VPRAG）以及基于纯文本指标且与人机评判结果校准的VPTT分数。实验表明人类评估、VLM评估与VPTT评估结果高度相关，验证了VPTT分数可作为可靠的感知代理指标。测试结果证明VPRAG在还原度与原创性之间达到最佳平衡，为个性化生成式AI提供了可扩展且保护隐私的技术基础。

English

We introduce the Visual Personalization Turing Test (VPTT), a new paradigm for evaluating contextual visual personalization based on perceptual indistinguishability, rather than identity replication. A model passes the VPTT if its output (image, video, 3D asset, etc.) is indistinguishable to a human or calibrated VLM judge from content a given person might plausibly create or share. To operationalize VPTT, we present the VPTT Framework, integrating a 10k-persona benchmark (VPTT-Bench), a visual retrieval-augmented generator (VPRAG), and the VPTT Score, a text-only metric calibrated against human and VLM judgments. We show high correlation across human, VLM, and VPTT evaluations, validating the VPTT Score as a reliable perceptual proxy. Experiments demonstrate that VPRAG achieves the best alignment-originality balance, offering a scalable and privacy-safe foundation for personalized generative AI.