Transcending Dimensions using Generative AI: Real-Time 3D Model Generation in Augmented Reality

Traditional 3D modeling requires technical expertise, specialized software, and time-intensive processes, making it inaccessible for many users. Our research aims to lower these barriers by combining generative AI and augmented reality (AR) into a cohesive system that allows users to easily generate, manipulate, and interact with 3D models in real time, directly within AR environments. Utilizing cutting-edge AI models like Shap-E, we address the complex challenges of transforming 2D images into 3D representations in AR environments. Key challenges such as object isolation, handling intricate backgrounds, and achieving seamless user interaction are tackled through advanced object detection methods, such as Mask R-CNN. Evaluation results from 35 participants reveal an overall System Usability Scale (SUS) score of 69.64, with participants who engaged with AR/VR technologies more frequently rating the system significantly higher, at 80.71. This research is particularly relevant for applications in gaming, education, and AR-based e-commerce, offering intuitive, model creation for users without specialized skills.

Generative AI Framework for 3D Object Generation in Augmented Reality

This thesis presents a framework that integrates state-of-the-art generative AI models for real-time creation of three-dimensional (3D) objects in augmented reality (AR) environments. The primary goal is to convert diverse inputs, such as images and speech, into accurate 3D models, enhancing user interaction and immersion. Key components include advanced object detection algorithms, user-friendly interaction techniques, and robust AI models like Shap-E for 3D generation. Leveraging Vision Language Models (VLMs) and Large Language Models (LLMs), the system captures spatial details from images and processes textual information to generate comprehensive 3D objects, seamlessly integrating virtual objects into real-world environments. The framework demonstrates applications across industries such as gaming, education, retail, and interior design. It allows players to create personalized in-game assets, customers to see products in their environments before purchase, and designers to convert real-world objects into 3D models for real-time visualization. A significant contribution is democratizing 3D model creation, making advanced AI tools accessible to a broader audience, fostering creativity and innovation. The framework addresses challenges like handling multilingual inputs, diverse visual data, and complex environments, improving object detection and model generation accuracy, as well as loading 3D models in AR space in real-time. In conclusion, this thesis integrates generative AI and AR for efficient 3D model generation, enhancing accessibility and paving the way for innovative applications and improved user interactions in AR environments.

Integrating Physiological Data with Large Language Models for Empathic Human-AI Interaction

This paper explores enhancing empathy in Large Language Models (LLMs) by integrating them with physiological data. We propose a physiological computing approach that includes developing deep learning models that use physiological data for recognizing psychological states and integrating the predicted states with LLMs for empathic interaction. We showcase the application of this approach in an Empathic LLM (EmLLM) chatbot for stress monitoring and control. We also discuss the results of a pilot study that evaluates this EmLLM chatbot based on its ability to accurately predict user stress, provide human-like responses, and assess the therapeutic alliance with the user.