Title:	Deep Rendering Graphics Pipeline
Authors:	Harris, Mark Wesley Semwal, Sudhanshu
Citation:	WSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 101-108.
Issue Date:	2021
Publisher:	Václav Skala - UNION Agency
Document type:	conferenceObject konferenční příspěvek
URI:	http://hdl.handle.net/11025/45014
ISBN:	978-80-86943-34-3
ISSN:	2464-4617 2464–4625(CD/DVD)
Keywords:	grafický kanál;vykreslovací technologie;strojové učení;zpracování obrazu;generativní kontradiktorní sítě;text na obrázek;sémantické zpracování dat
Keywords in different language:	graphics pipeline;rendering technologies;machine learning;image processing;generative adversarial networks;text-to-image;semantic data processing
Abstract in different language:	The graphics rendering pipeline is key to generating realistic images, and is a vital process of computational design,modeling, games, and animation. Perhaps the largest limiting factor of rendering is time; the processing requiredfor each pixel inevitably slows down rendering and produces a bottleneck which limits the speed and potential ofthe rendering pipeline. We applied deep generative networks to the complex problem of rendering an animated 3Dscene. Novel datasets of annotated image blocks were used to train an existing attentional generative adversarialnetwork to output renders of a 3D environment. The annotated Caltech-UCSD Birds-200-2011 dataset served asa baseline for comparison of loss and image quality. While our work does not yet generate production qualityrenders, we show how our method of using existing machine learning architectures and novel text and imageprocessing has the potential to produce a functioning deep rendering framework.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	WSCG 2021: Full Papers Proceedings

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