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arXiv · 2601.22680

Visual Personalization Turing Test

Rameen Abdal·James Burgess·Sergey Tulyakov·Kuan-Chieh Jackson Wang
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Abstract

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.

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Rameen Abdal, James Burgess, Sergey Tulyakov, Kuan-Chieh Jackson Wang. 2026-01-30. Visual Personalization Turing Test. https://arxiv.org/abs/2601.22680

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