Mapping Materials to 3D Texture Field  using GANs

Novosad, Markiian

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dc.contributor.author	Novosad, Markiian
dc.date.accessioned	2024-02-14T11:53:18Z
dc.date.available	2024-02-14T11:53:18Z
dc.date.issued	2022
dc.identifier.citation	Novosad, Markiian. Mapping Materials to 3D Texture Field using GANs / Novosad, Markiian; Supervisor: Vladyslav Zavadskyi; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2022. – 39 p.	uk
dc.identifier.uri	https://er.ucu.edu.ua/handle/1/4436
dc.language.iso	en	uk
dc.title	Mapping Materials to 3D Texture Field using GANs	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	3D Texture Synthesis is a broad research field which lets content creators get visu- ally satisfying look of the 3D object with minimal effort. However it is not so easy to achieve satisfying result coupled with computational efficiency. Many of the pro- posed methods are either computationally expensive and inefficient, or not capable of generating realistic visual appearance. To address this issue, we propose a new method – Mapping 2D Materials to 3D Texture Field using GANs. This method is based on Neural Implicit Representation network, thus able to internally represent a Texture Field without a need for storing additional information. This ability lets our method to be computationally inexpensive, theoretically being able to render the texture in real time. On the other hand, due to Generative Adversarial training strategy, our method is able to achieve highly realistic, visually satisfying looks. In this study we will describe our approach in detail.	uk