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.

中文翻译：

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基于草图的法线贴图生成和几何采样

法线贴图是表示复杂3D模型的重要且有效的方法。设计人员可能会从3D内容创建中的手绘草图中自动生成高质量和准确的法线贴图中受益。本文提出了一种深层生成模型，用于从用户草图和几何采样生成法线贴图。我们的生成模型基于条件生成对抗网络，其中条件蒙版的曲率敏感点采样。这种采样过程可以帮助消除生成结果作为网络输入的歧义。此外，我们采用了U-Net结构鉴别器，以帮助发电机得到更好的培训。验证了所提出的框架可以生成更准确的法线图。