  We present a cascaded diffusion model based on a part-level implicit 3D
representation. Our model achieves state-of-the-art generation quality and also
enables part-level shape editing and manipulation without any additional
training in conditional setup. Diffusion models have demonstrated impressive
capabilities in data generation as well as zero-shot completion and editing via
a guided reverse process. Recent research on 3D diffusion models has focused on
improving their generation capabilities with various data representations,
while the absence of structural information has limited their capability in
completion and editing tasks. We thus propose our novel diffusion model using a
part-level implicit representation. To effectively learn diffusion with
high-dimensional embedding vectors of parts, we propose a cascaded framework,
learning diffusion first on a low-dimensional subspace encoding extrinsic
parameters of parts and then on the other high-dimensional subspace encoding
intrinsic attributes. In the experiments, we demonstrate the outperformance of
our method compared with the previous ones both in generation and part-level
completion and manipulation tasks.
