  Semantic-driven 3D shape generation aims to generate 3D objects conditioned
on text. Previous works face problems with single-category generation,
low-frequency 3D details, and requiring a large number of paired datasets for
training. To tackle these challenges, we propose a multi-category conditional
diffusion model. Specifically, 1) to alleviate the problem of lack of
large-scale paired data, we bridge the text, 2D image and 3D shape based on the
pre-trained CLIP model, and 2) to obtain the multi-category 3D shape feature,
we apply the conditional flow model to generate 3D shape vector conditioned on
CLIP embedding. 3) to generate multi-category 3D shape, we employ the
hidden-layer diffusion model conditioned on the multi-category shape vector,
which greatly reduces the training time and memory consumption.
