Sketch2Normal : Deep Networks for Normal Map Generation

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings of SA ’17 SIGGRAPH Asia 2017 Posters
PublisherACM 
ISBN (Print)978-1-4503-5405-9
Publication statusPublished - Nov 2017

Conference

TitleSIGGRAPH Asia 2017 (SA '17)
PlaceThailand
CityBangkok
Period27 - 30 November 2017

Abstract

Normal maps are of great importance for many 2D graphics applications such as surface editing, re-lighting, texture mapping and 2D shading etc. Automatically inferring normal map is highly desirable for graphics designers. Many researchers have investigated the inference of normal map from intuitive and flexiable line drawing based on traditional geometric methods while our proposed deep networks-based method shows more robustness and provides more plausible results.

Research Area(s)

  • Generative Adversarial Network, Normal Map, Sketch

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Sketch2Normal : Deep Networks for Normal Map Generation. / Su, Wanchao; Yang, Xin; Fu, Hongbo.

Proceedings of SA ’17 SIGGRAPH Asia 2017 Posters. ACM , 2017. 34.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review