Due to the scarcity of point cloud datasets in a specific domain, utilizing generative model approaches becomes essential for data augmentation. Diffusion models have demonstrated impressive capabilities in data generation through a guided reverse process. In this work, we employ a reverse process of a Markov chain conditioned on shape latent to progressively generate dental crown point cloud from a noise distribution. We propose to map the global shape latent to a set of partlevel
implicit representations and introduce a cross-attention block to provide geometric structural information for point cloud generation. We conduct a series of experiments on a real dental crown dataset, and the experimental results show certain improvement compared to the baselines, demonstrating the efficacy of our approach. In experiments, we present the capability of our method to generate large-scale dental crown models through unsupervised learning, effectively enriching the existing dental crown dataset.