ABSTRACT. The purpose of this study is to examine 3D virtual garment simulation and augmented reality-based body-enhancing technologies, neuromorphic tactile recognition and in-sensor motion perception systems, and 3D facial avatar makeup simulation and digital twin-based detailed body shape representation tools for idealized beauty images. The analysis highlights that face and body emotion recognition processes necessitate digital beauty and computer-generated imagery technologies, motion-based gesture recognition and artificial tactile sensory systems, and 3D facial avatar makeup simulation and digital twin-based detailed body shape representation tools. Data extraction and reporting tools, reference management software, and machine learning classifiers harnessed include BIBOT, CASP, DistillerSR, Eppi-Reviewer, JBI SUMARI, and SluRp. The case studies cover YouCam Perfect app, Sendbird, Banuba’s Face Filters SDK beautification tool, CyberLink’s YouCam 365, Leopard V7 Beauty Camera & Sweet Camera,YouCam Makeup artificial intelligence face editing app, and Meitu artificial intelligence face-reshaping and beautification tool.

Keywords: 3D virtual garment; augmented reality-based body-enhancing; in-sensor motion perception; 3D facial avatar makeup; digital twin-based detailed body shape representation; idealized beauty image

How to cite: Szydło, J., Bonciu, C. I., Burcea, Ş. G., and Dumitrescu, C.-O. (2025). “3D Virtual Garment Simulation and Augmented Reality-based Body-enhancing Technologies, Neuromorphic Tactile Recognition and In-Sensor Motion Perception Systems, and 3D Facial Avatar Makeup Simulation and Digital Twin-based Detailed Body Shape Representation Tools for Idealized Beauty Images,” Journal of Research in Gender Studies 15(1): 41–48. doi: 10.22381/JRGS15120254.

Received 11 February 2025 • Received in revised form 20 July 2025
Accepted 23 July 2025 • Available online 25 July 2025