  We propose WarpingGAN, an effective and efficient 3D point cloud generation
network. Unlike existing methods that generate point clouds by directly
learning the mapping functions between latent codes and 3D shapes, Warping-GAN
learns a unified local-warping function to warp multiple identical pre-defined
priors (i.e., sets of points uniformly distributed on regular 3D grids) into 3D
shapes driven by local structure-aware semantics. In addition, we also
ingeniously utilize the principle of the discriminator and tailor a stitching
loss to eliminate the gaps between different partitions of a generated shape
corresponding to different priors for boosting quality. Owing to the novel
generating mechanism, WarpingGAN, a single lightweight network after one-time
training, is capable of efficiently generating uniformly distributed 3D point
clouds with various resolutions. Extensive experimental results demonstrate the
superiority of our WarpingGAN over state-of-the-art methods in terms of
quantitative metrics, visual quality, and efficiency. The source code is
publicly available at https://github.com/yztang4/WarpingGAN.git.
