Procedural Tree Generation
How to efficiently predict branching structures from foliage?
A. Taklimi (TU Delft - Electrical Engineering, Mathematics and Computer Science)
E Eisemann (TU Delft - Computer Graphics and Visualisation)
Petr Kellnhofer (TU Delft - Computer Graphics and Visualisation)
L. Uzolas (TU Delft - Computer Graphics and Visualisation)
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Abstract
The objective of this project is to train a model that transforms a tree with its foliage into only its branch structure. This is achieved by employing machine-learning techniques, specifically Generative Adverserial Networks (GANs). By utilizing the proposed method, a predictive model is built that automatically minimizes its own error function through a comparison of a set of input and ground-truth tree images, which are tree images with and without leaves, respectively. The adoption of GANs has shown promising results, both visually and metrically.