Authoring and Experiencing Virtual 3D Environments

The growing popularity of Virtual Reality and Augmented Reality has led to an increase in demand for 3D content. However, traditional methods for creating 3D environments are often expensive and complicated, making it difficult for non-expert users to produce their own content. This thesis addresses this issue through research focusing on two main areas: (1) the study of 3D user experiences, which helps create engaging and immersive experiences that meet users' needs and preferences and (2) increasing accessibility for content generation, which divides into creating approachable 3D user interfaces and intelligent creative tools. Our research aims to understand how users perceive and interact with immersive environments and, subsequently, to develop tools that enable users to create 3D content with ease and accessibility, even with limited experience or resources, with the help of generative models in machine learning and intuitive UI. Our project PanoTrace introduces a 3D modeling platform in VR for creating 3D scenes from 2D panoramas, providing a first example of our idea of novel approachable 3D user interfaces. Our wide-area VR walking study on the other hand falls entirely within the domain of studying 3D user experiences. It investigates the impact of natural walking versus teleportation on presence and user preference in VR experiences. We introduce the content-aware semantic editing and inpainting system (CASEIn) as an example of an intelligent creative tool. CASEIn generates high-quality results for guided image inpainting and semantic image synthesis using machine learning. Our projects DeepDive and Faded focus on accessibility for content generation by linking approachable UI design with machine learning. Faded is a memory reconstruction system that relies on our inpainting model, CASEIn, to extrapolate existing sparse information, including images and the user's memory of a space, into a cohesive 3D experience. In summary, this thesis demonstrates the fe