Název:	A Mobile Augmented Reality Application For Simulating Claude Monet’s Impressionistic Art Style
Autoři:	Del Gallego, Neil Patrick Viaje, Cedric Lance Gerra-Clarin, Michael Ryan Roque, John Marvic Non, Gary Steven Martinez, Jesin Jarod Gana, Jose Antonio
Citace zdrojového dokumentu:	WSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 81-90.
Datum vydání:	2021
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	conferenceObject konferenční příspěvek
URI:	http://hdl.handle.net/11025/45012
ISBN:	978-80-86943-34-3
ISSN:	2464-4617 2464–4625(CD/DVD)
Klíčová slova:	rozšířená realita;mobilní zařízení;obrazový filtr;stylizace obrazu;přenos stylu;malířské ztvárnění
Klíčová slova v dalším jazyce:	augmented reality;mobile devices;image filter;image stylization;style transfer;painterly rendering
Abstrakt v dalším jazyce:	In this study, we showcase a mobileaugmented reality application where a user places various 3D models in atabletop scene. The scene is captured and then rendered as Claude Monet’s impressionistic art style. One possibleuse case for this application is to demonstrate the behavior of the impressionistic art style of Claude Monet, byapplying this to tabletop scenes, which can be useful especially for art students. This allows the user to create theirown "still life" composition and study how the scene is painted. Our proposed framework is composed of threesteps. The system first identifies the context of the tabletop scene, through GIST descriptors, which are used asfeatures to identify the color palette to be used for painting. Our application supports three different color palettes,representing different eras of Monet’s work. The second step performs color mixing of two different colors in thechosen palette. The last step involves applying a three-stage brush stroke algorithm where the image is renderedwith a customized brush stroke pattern applied in each stage. While deep learning techniques are already capableof performing style transfer from paintings to real-world images, such as the success of CycleGAN, results showthat our proposed framework achieves comparable performance to deep learning style transfer methods on tabletopscenes.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	WSCG 2021: Full Papers Proceedings

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.