Do Generative Priors Align with Human Naturalness Perception?
Read the original on arXiv Computer Vision →The study investigates whether the priors learned by visual generative models align with human perceptions of image naturalness. By measuring prediction errors across 25 image and video generators and using paired relational interventions that alter facial configurations or illumination while preserving low-level statistics, the authors find that loss differences replicate human-like sensitivities, including the Thatcher effect and shape-dependent illumination responses. These loss differences correlate strongly with continuous human naturalness judgments (up to r = 0.84 for faces) and remain informative after controlling for feature distances and standard image quality metrics, revealing that human-like naturalness judgments can dissociate from generic violation detection.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.