PyData Yerevan 2022

Hovhannes Margaryan

Hovhannes Margaryan has a bachelor's degree in Computer Science from the American University of Armenia and is currently a master's student in Data Science at KU Leuven and ML Scientist at Picsart's Creative Intelligence team. Hovhannes is interested in different areas of computer vision and has experience with texture synthesis, style transfer, facial inpainting, image outpainting, full body generation, vector graphics, and physical simulation.

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Classical Texture Synthesis and Beyond
Hovhannes Margaryan

Given a small example of a texture as an input the goal of texture synthesis is to generate an output image that is an expanded and smartly tiled version of the given input maintaining perceptual information. Texture synthesis methods are categorized into three main types: non-parametric, parametric and procedural methods. Two of these categories namely non-parametric and parametric are discussed during the talk. On the one hand, non-parametric approaches resample pixels or patches from the given source texture. Texture Optimization for Example-based Synthesis and Image Quilting for Texture Synthesis and Transfer are discussed. On the other hand, parametric methods require an explicit definition of a parametric texture. Two parametric methods, namely Texture Synthesis using CNNs and Non-Stationary Texture Synthesis by Adversarial Expansion are presented during the presentation. Results of the above-mentioned methods are demonstrated as a conclusion.

213W PAB