Date of Award

12-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

School of Computing

Committee Chair/Advisor

Matias Volonte

Committee Member

Andrew T. Duckowski

Committee Member

Andrew Robb

Committee Member

Nina A. Gehrer

Abstract

In an era where digital and physical realities increasingly intertwine, the perception of body image is undergoing a significant transformation. Traditional understandings of body dissatisfaction, long studied in relation to psychological distress and eating disorders, are now being reshaped by technologies such as Virtual Reality (VR), Augmented Reality (AR), and Artificially Intelligent (AI)- generated media. These technologies have introduced novel ways of experiencing and interacting with the human form, raising critical questions about their impact on self-perception and internalization of beauty standards.

As virtual representations become more prevalent in entertainment, social media, and interactive platforms, it is becoming more crucial to develop ethical, unbiased, and standardized tools for evaluating their psychological and social impact. One area of particular concern is body image, which is shaped by a complex interplay of factors including age, gender, sex, race, socioeconomic status, and cultural background. Emerging research continues to uncover disparities in body dissatisfaction across these demographics, highlighting the need for more inclusive research methodologies and tools capable of accounting for individual differences.

This dissertation introduces and validates a novel real-time AR-based evaluation system to measure body image concerns through interactions with full-body Virtual Humans (VHs). Leveraging recent advancements in Head-Mounted Displays (HMDs) and integrated eye-tracking technology, the system collects rich objective data, including first-order (fixations, saccades, dwell time) and second-order (transition matrices, gaze entropy) gaze metrics, to provide insights into visual attention patterns and implicit behaviors linked to body dissatisfaction.

Building on prior work involving standardized Areas of Interest (AOIs) for facial and body regions, and studies of gaze behavior across demographic concordance (e.g., race and sex) using Microsoft Rocketbox avatars, this research extends the open-source ReplEye pipeline. Developed in Unity using the Microsoft Mixed Reality Toolkit 3 (MRTK3) and Microsoft HoloLens 2 (HL2), ReplEye supports reproducible, AOI-driven analysis of eye-tracking data. The pipeline enables both researchers and novices to rapidly deploy immersive studies with rigorously defined body-based AOIs.

A study is laid out that describes how this AR system can allow participants to interact with VHs under varying conditions of agency (whether they can control the avatar’s movements) and personalization (similarity of the VH’s appearance to the participant). These manipulations enable investigation of how embodiment, control, and self-resemblance influence body image. The study hypothesizes that increased personalization and agency will correlate with reduced visual scanning of traditionally scrutinized, and uniquely internalized body areas, greater self-reported body satisfaction, and improved self-esteem.

Preliminary findings and pilot testing support the system’s feasibility for future applications in body image research and intervention. The modular architecture also facilitates testing across varied body types (e.g., slimmer, curvier, more muscular), providing a flexible foundation for broader psychological exploration. Ultimately, this dissertation aims to demonstrate that interactive, personalized virtual representations can be used as a tool for intervention therapies to promote healthier body image perceptions in the age of digitization.

This dissertation intends to serve as the groundwork for advanced, interactive, virtual systems that strive to promote positive body perceptions and disavow unrealistic ones, therefore reshaping how we internalize our Digital Reflections.

Comments

ReplEye Github Link: https://github.com/DeyrelDiaz/ReplEye

Author ORCID Identifier

https://orcid.org/0000-0003-4060-2121

Download

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.