Humans tend to form quick subjective first impressions of non-physical
attributes when seeing someone's face, such as perceived trustworthiness or
attractiveness. To understand what variations in a face lead to different
subjective impressions, this work uses generative models to find semantically
meaningful edits to a face image that change perceived attributes. Unlike prior
work that relied on statistical manipulation in feature space, our end-to-end
framework considers trade-offs between preserving identity and changing
perceptual attributes. It maps identity-preserving latent space directions to
changes in attribute scores, enabling transformation of any input face along an
attribute axis according to a target change. We train on real and synthetic
faces, evaluate for in-domain and out-of-domain images using predictive models
and human ratings, demonstrating the generalizability of our approach.
Ultimately, such a framework can be used to understand and explain biases in
subjective interpretation of faces that are not dependent on the identity.

使用生成模型找到脸部图像中感知属性的语义编辑，综合考虑保持身份和改变感知属性之间的权衡，以改变脸部图像中的任何输入脸沿属性轴的转换。通过预测模型和人类评分，在真实和合成脸上进行培训和评估，证明了我们方法的普适性，最终可用于理解和解释与身份无关的对面部主观解释中的偏见。