Visual content has the ability to convey and impact human emotions. It is crucial to understanding the emotions being communicated and the ways in which they are implied by the visual elements in images. This study evaluates the aesthetic emotion of portrait art generated by our Generative AI Portraiture System. Using the Visual Aesthetic Wheel of Emotion (VAWE), aesthetic responses were documented and subsequently analyzed using heatmaps and circular histograms with the aim of identifying the emotions evoked by the generated portrait art. The data from 160 participants were used to categorize and validate VAWE’s 20 emotions with selected AI portrait styles. The data were then used in a smaller self-portrait qualitative study to validate the developed prototype for an Emotionally Aware Portrait System, capable of generating a personalized stylization of a user’s self-portrait, expressing a particular aesthetic emotional state from VAWE. The findings bring forth a new vision towards blending affective computing with computational creativity and enabling generative systems with awareness in terms of the emotions they wish their output to elicit.
视觉内容具有传达并影响人类情绪的能力。理解视觉作品所传递的情绪，以及这些情绪如何通过图像中的视觉元素被暗示和建构，对于相关研究具有重要意义。本研究评估了由我们开发的生成式人工智能肖像系统（Generative AI Portraiture System）所生成肖像艺术的审美情绪。研究采用“视觉审美情绪轮”（Visual Aesthetic Wheel of Emotion, VAWE）记录参与者的审美反应，并进一步通过热图与环形直方图对数据进行分析，以识别这些生成式肖像艺术所唤起的情绪类型。基于160名参与者的数据，研究对VAWE所包含的20种情绪在特定AI肖像风格中的适用性进行了分类与验证。随后，这些数据又被应用于一个规模较小的自画像质性研究中，以验证所开发的“情绪感知肖像系统”（Emotionally Aware Portrait System）原型。该系统能够依据VAWE中的特定审美情绪状态，对用户的自画像进行个性化风格化生成，从而表达预期的审美情绪。本研究结果提出了一种新的发展方向，即将情感计算与计算创造力相结合，并推动生成系统具备对其输出所欲唤起情绪的“感知能力”与目标导向意识。
