  Realistic and diverse 3D shape generation is helpful for a wide variety of
applications such as virtual reality, gaming, and animation. Modern generative
models, such as GANs and diffusion models, learn from large-scale datasets and
generate new samples following similar data distributions. However, when
training data is limited, deep neural generative networks overfit and tend to
replicate training samples. Prior works focus on few-shot image generation to
produce high-quality and diverse results using a few target images.
Unfortunately, abundant 3D shape data is typically hard to obtain as well. In
this work, we make the first attempt to realize few-shot 3D shape generation by
adapting generative models pre-trained on large source domains to target
domains using limited data. To relieve overfitting and keep considerable
diversity, we propose to maintain the probability distributions of the pairwise
relative distances between adapted samples at feature-level and shape-level
during domain adaptation. Our approach only needs the silhouettes of few-shot
target samples as training data to learn target geometry distributions and
achieve generated shapes with diverse topology and textures. Moreover, we
introduce several metrics to evaluate the quality and diversity of few-shot 3D
shape generation. The effectiveness of our approach is demonstrated
qualitatively and quantitatively under a series of few-shot 3D shape adaptation
setups.
