Reconstruction, Generation and Exploration of Digital Content with Deep Neural Features
X. Luo (TU Delft - Computer Graphics and Visualisation)
Elmar Eisemann – Promotor (TU Delft - Computer Graphics and Visualisation)
Ricardo Marroquim – Copromotor (TU Delft - Computer Graphics and Visualisation)
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Abstract
This dissertation investigates the use of deep-learning methods for user-guided content creation, manipulation, and exploration. It illustrates the potential of neural techniques to support working with large data collections and we illustrate our solutions through several applications. Regarding content generation, we propose an algorithm to produce material representations from a single image. We illustrate content manipulation with an approach to perform perceptually plausible interpolation and examine exploration in the context of interactive retrieval. For the latter, we show that features spaces are of high relevance to organize data and show the generality of this concept by proposing novel exploration methods for image and music collections.