Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 6 of 6 for “"Visual synthesis"”.
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Controllable Visual Synthesis
… we address the problem of controllable image synthesis. We propose several approaches that allow the user to synthesize realistic content and manipulate images to achieve their desired goals with ease and flexibility.
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Learning Consistent Visual Synthesis
… or edit the 2D observation afterward. Thus, visual content editing and synthesis become an essential task in computer vision. However, achieving high-quality visual synthesis often requires a complex and expensive multi-camera setup. This is not practical for daily use because most people …
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Learning for Visual Synthesis and Transformation
Visual synthesis is one of the most fundamental problems in computer vision and artificial intelligence. Visual synthesis aims to create pixel-level data (e.g., images and videos) based on descriptions such as texts, noise, semantic annotations and images. Recently, deep generative learning has …
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Efficient Generative Models for Visual Synthesis
While current visual generative models produce high-quality outputs, they suffer from significant computational costs and latency, limiting their applicability in interactive settings. In this dissertation, we introduce a suite of techniques designed to enhance the efficiency of generative models …
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Visualizing Ancient Empire in Tudor England: Imperial Monarchy, Reformation, and the Antique Soldier in the Title Page to Richard Grafton’s Large Chronicle (1569)
<p>This thesis analyzes the iconography and visual sources of the title page to the first volume of <em>A chronicle at large and meere history of the affayres of Englande </em>(1569) by the Tudor author Richard Grafton. Representing the visual synthesis of several distinct but interrelated currents …
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Visual analysis and synthesis with physically grounded constraints
… these limitations by modeling and interpreting visual signals in terms of physical surface, object, and scene. I show how we can incorporate physically grounded constraints such as scene-specific geometry in a non-parametric optimization framework for (1) revealing the missing parts of an image …