Scaling resolution via guided priors: from explicit multimodal fusion to inference-time adaptation of generative foundation models

Tragakis, Athanasios (2026) Scaling resolution via guided priors: from explicit multimodal fusion to inference-time adaptation of generative foundation models. PhD thesis, University of Glasgow.

Abstract

Achieving high spatial resolution remains a fundamental challenge in computer vision,
where results often fail to adhere to physical constraints. This thesis investigates the use of guided priors to mitigate these limitations, tracing a methodological evolution from explicit multimodal fusion in supervised settings to implicit inference-time priors
within generative foundation models across 2D and 3D domains.
First, we address depth sensor resolution limits through explicit guidance. We propose
IGAF (Incremental Guided Attention Fusion), a deep learning architecture that utilizes high-resolution RGB images to iteratively upsample depth maps. By selectively attending to cross-modal features, IGAF transfers structural guidance from the RGB domain to reconstruct fine geometric details that exceed the sensors native resolution.
Shifting toward generative AI, we introduce Pixelsmith, a zero-shot framework for gigapixel image generation on a single GPU. By employing a cascading approach where lower-resolution outputs serve as explicit guidance for patch-based denoising, we leverage the foundation models learned knowledge as an implicit prior. This enables the synthesis of extreme detail while overcoming the memory constraints of standard diffusion models.
Finally, we extend this paradigm to 3D with SceneHI, a framework that lifts 2D generative features into 3D space. By orchestrating a rendering / inverse rendering loop as explicit guidance to adapt implicit priors, SceneHI eliminates multi-view inconsistencies.
We further integrate geometry-consistent shadows, addressing both resolution and physical plausibility constraints in complex 3D environments.
Ultimately, this thesis establishes that structural guidance is an essential requirement
for surpassing the physical resolution limits of sensors and foundation models across 2D and 3D domains.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: Q Science > QA Mathematics > QA76 Computer software
• Colleges/Schools: College of Science and Engineering > School of Physics and Astronomy
• Supervisor's Name: Faccio, Professor Daniele
• Date of Award: 2026
• Depositing User: Theses Team
• Unique ID: glathesis:2026-86161
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 05 Aug 2026 11:39
• Last Modified: 05 Aug 2026 13:39
• Thesis DOI: 10.5525/gla.thesis.86161
• URI: https://theses.gla.ac.uk/id/eprint/86161

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