A single 3D shape wavelet-based generative model

Hao Huang, Shuaihang Yuan, Zheng Peng, Yu Hao, Congcong Wen, Yi Fang

Abstract: Highlights•We propose a wavelet generative model to generate novel shapes with a single input.•We adapt data augmentation and Coulomb adversarial loss to facilitate training.•Experiments show that our model can generate diverse 3D shapes in various categories.