  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.
