Sketch2Normal : Deep Networks for Normal Map Generation
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | Proceedings of SA ’17 SIGGRAPH Asia 2017 Posters |
| Publisher | Association for Computing Machinery (ACM) |
| ISBN (print) | 978-1-4503-5405-9 |
| Publication status | Published - Nov 2017 |
Conference
| Title | SIGGRAPH Asia 2017 (SA '17) |
|---|---|
| Place | Thailand |
| City | Bangkok |
| Period | 27 - 30 November 2017 |
Link(s)
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. Association for Computing Machinery (ACM), 2017. 34.
Proceedings of SA ’17 SIGGRAPH Asia 2017 Posters. Association for Computing Machinery (ACM), 2017. 34.
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
