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.
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