ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore 3D body scanning and deep learning-based multimodal emotion recognition technologies, object detection and categorization algorithms, and augmented reality face filters. Throughout June 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “body-image affective judgments” and “dysfunctional appearance beliefs” + “3D virtual garment simulation and facial expression recognition technologies,” “beauty filter-based appealing self-presentation and generative artificial intelligence virtual try-on tools,” and “deep learning-based gesture recognition and augmented reality face makeup systems.” As research published between 2017 and 2022 was inspected, only 170 articles satisfied the eligibility criteria, and 25 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.

Keywords: 3D virtual garment simulation; beauty filter-based appealing self-presentation; virtual try-on tool; body-image affective judgment

How to cite: Mihăilă, R. (2023). “3D Virtual Garment Simulation and Facial Expression Recognition Technologies, Beauty Filter-based Appealing Self-presentation and Generative Artificial Intelligence Virtual Try-on Tools, and Deep Learning-based Gesture Recognition and Augmented Reality Face Makeup Systems for Body-Image Affective Judgments and Dysfunctional Appearance Beliefs,” Journal of Research in Gender Studies 13(2): 54–68. doi: 10.22381/JRGS13220234.

Received 28 July 2023 • Received in revised form 24 December 2023
Accepted 29 December 2023 • Available online 30 December 2023