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.
@article{arxiv.2104.11554,
title = {Sketch-based Normal Map Generation with Geometric Sampling},
author = {Yi He and Haoran Xie and Chao Zhang and Xi Yang and Kazunori Miyata},
journal= {arXiv preprint arXiv:2104.11554},
year = {2021}
}
Comments
accepted in International Workshop on Advanced Image Technology 2021, 5 pages, 2 figures