Sketch-based normal map generation with geometric sampling

Yi He; Haoran Xie; Chao Zhang; Xi Yang; Kazunori Miyata

doi:10.1117/12.2590760

13 March 2021 Sketch-based normal map generation with geometric sampling

Yi He, Haoran Xie, Chao Zhang, Xi Yang, Kazunori Miyata

Proceedings Volume 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021; 117661B (2021) https://doi.org/10.1117/12.2590760
Event: International Workshop on Advanced Imaging Technology 2021 (IWAIT 2021), 2021, Online Only

Abstract

Normal map is an important and efficient way to represent complex 3D models. A designer may benefit from the auto-generation of high quality and accurate normal maps from freehand sketches in 3d content creation. This paper proposes a deep generative model for generating normal maps from users’ sketch with geometric sampling. Our generative model is based on conditional generative adversarial network with the curvature-sensitive points sampling of conditional masks. This sampling process can help eliminate the ambiguity of generation results as network input. It is verified that the proposed framework can generate more accurate normal maps.

Citation Download Citation

Yi He, Haoran Xie, Chao Zhang, Xi Yang, and Kazunori Miyata "Sketch-based normal map generation with geometric sampling", Proc. SPIE 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021, 117661B (13 March 2021); https://doi.org/10.1117/12.2590760

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