Sketch-based Normal Map Generation with Geometric Sampling

• URL: http://arxiv.org/abs/2104.11554v1
• Date: Fri, 23 Apr 2021 12:30:22 GMT
• Title: Sketch-based Normal Map Generation with Geometric Sampling
• Authors: Yi He, Haoran Xie, Chao Zhang, Xi Yang, Kazunori Miyata
• Abstract summary: 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.
• Score: 14.323902770651289
• License: http://creativecommons.org/licenses/by/4.0/
• 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. In addition, we adopted a U-Net structure discriminator to help the generator be better trained. It is verified that the proposed framework can generate more accurate normal maps.

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