Access Type
Open Access Dissertation
Date of Award
January 2025
Degree Type
Dissertation
Degree Name
Ph.D.
Department
Computer Science
First Advisor
Jing Hua
Abstract
Generative Artificial Intelligence (AI) has transformed how we synthesize, edit, and manipulate complex data, opening new possibilities for immersive digital content. This dissertation, titled “Advancing Generative AI in 3D and 4D Spaces,” investigates the capabilities and limitations of state-of-the-art generative models in static 3D scenes and time-aware 4D environments. In the 3D setting, scene editing is hindered by multi-view inconsistency and the high computational cost of per-scene retraining. To address these issues, the work introduces Free-Editor, a training free approach that utilizes an Edit Transformer to propagate a single edited view across all camera perspectives without additional optimisation, delivering prompt faithful edits up to twenty times faster than contemporary baselines while preserving geometric fidelity. Extending into the 4D domain, the dissertation confronts the added complexity of temporal coherence. A new framework, PSF-4D, adapts diffusion-based generation to dynamic scenes through progressive, correlated noise sampling that couples spatial and temporal information, enabling coherent local edits, style transfers, and object removals throughout video-volumes. Together, these contributions push generative AI beyond static content creation toward real-time, spatiotemporally consistent scene manipulation, with broad implications for virtual production, robotics simulation, and immersive analytics.
Recommended Citation
Iqbal, Hasan, "Advancing Generative Ai In 3d And 4d Spaces" (2025). Wayne State University Dissertations. 4283.
https://digitalcommons.wayne.edu/oa_dissertations/4283