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.

翻译：普通地图是代表复杂的三维模型的一种重要而有效的方法。 设计师可以从3D内容创建中的自手草图自动生成高质量和准确的普通地图中受益。 本文提出了一个从用户中生成普通地图并进行几何抽样的深层基因模型。 我们的基因模型以有条件的面罩曲线敏感点取样的 " 条件性基因反向网络 " 为基础。 这个取样过程有助于消除作为网络输入的生成结果的模糊性。 此外,我们采用了U- Net结构区分器帮助发电机得到更好的培训。 经核实,拟议的框架能够产生更准确的正常地图。