ABSTRACT. Despite the relevance of convolutional neural network-based gesture recognition and virtual try-on clothing systems, 3D digital garment photorealistic simulations, and wearable artificial intelligence and aesthetic self-monitoring devices for beauty filter-based appealing self-presentations and social appearance comparisons, only limited research has been conducted on this topic. The findings and analyses highlight that 3D virtual modeling and multi-sensory perception technologies, synthetic speech augmentation and aesthetic self-monitoring devices, and visual sentiment and emotion recognition algorithms enable facial cue and expression detection and visual body processing mechanisms for social media-related body dissatisfaction, dysfunctional appearance beliefs, and negative moods and emotions. Machine learning-based study selection process and text mining systematic review management software and tools leveraged include CASP, R package and Shiny app citationchaser, Eppi-Reviewer, METAGEAR package for R, and PICO Portal. The case studies cover Perfect Me, GlamAR.io, PicsArt, Facetune, Peachy, Snapseed AI, AirBrush, YouCam, and PrettyUp.

Keywords: gesture recognition; virtual try-on clothing; 3D digital garment; aesthetic self-monitoring; beauty filter-based appealing self-presentation; social appearance

How to cite: Ciurlău, C. F., Emek, M. L., Dragomir, M., and Drugău Constantin, A.-L. (2025). “Convolutional Neural Network-based Gesture Recognition and Virtual Try-on Clothing Systems, 3D Digital Garment Photorealistic Simulations, and Wearable Artificial Intelligence and Aesthetic Self-Monitoring Devices for Beauty Filter-based Appealing Self-Presentations and Social Appearance Comparisons,” Journal of Research in Gender Studies 15(1): 57–64. doi: 10.22381/JRGS15120256.

Received 18 February 2025 • Received in revised form 11 July 2025
Accepted 17 July 2025 • Available online 23 July 2025