Research Introduction
FasT Lab은 의류학적 지식을 인공지능과 디지털 기술을 통해 확장하고 재해석하는 연구를 수행합니다.
가상 의류 시스템, AI 기반 스타일 지능, 스마트 생산 환경, 그리고 인간 중심 데이터 분석을 주요 연구 분야로 삼고 있습니다.
의류·텍스타일 전문성과 컴퓨팅 기술을 융합하여, AI 전환 시대의 패션 산업에 실질적인 해결책을 제시하는 것을 목표로 합니다.
At FasT Lab, we investigate how fashion knowledge can be extended and transformed through artificial intelligence and digital technologies.
Our research focuses on virtual garment systems, AI-driven style intelligence, smart production environments, and human-centered data analysis.
By integrating clothing and textile expertise with computational modeling and immersive technologies, we aim to develop practical and meaningful solutions for the AI-driven transformation of the fashion industry.
01
Research on Interactive virtual fitting
We investigate how digital technologies can realistically reproduce the experience of trying on garments.
Online fashion platforms often lack accurate visualization of fit, silhouette, and body interaction. Our research develops interactive virtual fitting systems by integrating 3D garment modeling, body representation, and real-time user interaction. Through AI-based simulation and human-centered interface design, we aim to enhance consumer confidence and improve decision-making in digital fashion environments.
02
Research on Style Recommendation AI
Fashion style is inherently subjective and influenced by personal preference, context, and emotional factors.
Our research explores AI-driven approaches to understand and model individual style by analyzing visual features, textual descriptions, and human behavioral data. Rather than focusing solely on prediction accuracy, we investigate how fashion knowledge and human-centered data can be integrated into intelligent recommendation systems. This work aims to provide more explainable, personalized, and context-aware style guidance in digital fashion environments.
Previous Works
01
Research on Emotion Recognition AI using Body Motion Data
This study proposed a method to visualize emotional body motion (BMM), and showed that emotional statuses can be distinguished using body motion data with deep learning model (CNN).
For more details, see "Bodily Sensation Map vs. Bodily Motion Map: Visualizing and Analyzing Emotional Body Motions (IEEE Transactions on Affective Computing, 2024)."
02
Research on Individualized Rendering Method for Virtual Reality
This study proposed a VR rendering method (Foveated Rendering) using individual's central and peripheral vision. Which effectively reduces computing power as well as preserving VR experience quality.
For more details, see “Individualized foveated rendering with eye-tracking head-mounted display (Virtual Reality, 2024).”
03
Research on Personalized Avatar
This study proposed a method to make a personalized avatar that resembles user's appearance of their face and body sizes using single smartphone. Which can provide high-quality virtual experiences.
For more details, see “Impact of Personalized Avatars and Motion Synchrony on Embodiment and Users’ Subjective Experience: Empirical Study (JMIR Serious Games, 2022).”
04
Research on measuring body size change perception
This study proposed a VR method to measure when an individual recognizes the size change of their body using a size-matched virtual avatar. We speculate that the proposed method can be related to one’s body image flexibility.
For more details, see “Measuring recognition of body changes over time: A human-computer interaction tool using dynamic morphing and body ownership illusion (PLoS One, 2020).”
05
Research on interaction between embodiment subcomponents
This study investigated the relationship between embodiment subcomponents of body ownership, agency and self-location using a point-light avatar. We found that these components can be used to step-wisely enhance user’s subjective virtual experience.
For more details, see “Controlling the sense of embodiment for virtual avatar applications: methods and empirical study (JMIR Serious Games, 2020).”
06
Research on Full-Body Ownership Ilusion
Recent advances in technology have allowed users to experience an illusory feeling of full body ownership of a virtual avatar. Such virtual embodiment has the power to elicit perceptual, behavioral, cognitive, and emotional changes related to oneself.
For more details, see “Full-body ownership illusion can change our emotion (ACM CHI, 2018).”