  We develop a generalized 3D shape generation prior model, tailored for
multiple 3D tasks including unconditional shape generation, point cloud
completion, and cross-modality shape generation, etc. On one hand, to precisely
capture local fine detailed shape information, a vector quantized variational
autoencoder (VQ-VAE) is utilized to index local geometry from a compactly
learned codebook based on a broad set of task training data. On the other hand,
a discrete diffusion generator is introduced to model the inherent structural
dependencies among different tokens. In the meantime, a multi-frequency fusion
module (MFM) is developed to suppress high-frequency shape feature
fluctuations, guided by multi-frequency contextual information. The above
designs jointly equip our proposed 3D shape prior model with high-fidelity,
diverse features as well as the capability of cross-modality alignment, and
extensive experiments have demonstrated superior performances on various 3D
shape generation tasks.
