We investigate the problem of training generative models on a very sparse collection of 3D models. We use geometrically motivated energies to augment and thus boost a sparse collection of example (training) models. We analyze the Hessian of the as-rigid-as-possible (ARAP) energy to sample from and project to the underlying (local) shape space, and use the augmented dataset to train a variational autoencoder (VAE). We iterate the process of building latent spaces of VAE and augmenting the associated dataset, to progressively reveal a richer and more expressive generative space for creating geometrically and semantically valid samples. Our framework allows us to train generative 3D models even with a small set of good quality 3D models, which are typically hard to curate. We extensively evaluate our method against a set of strong baselines, provide ablation studies and demonstrate application towards establishing shape correspondences. We present multiple examples of interesting and meaningful shape variations even when starting from as few as 3-10 training shapes.

翻译：我们用几何驱动的能量来增加并从而增加少量的范例(培训)模型。 我们用微小的3D质量模型来培训3D型基因模型,这些模型通常很难校正。 我们用一套强大的基线来广泛评估我们的方法,提供调节研究,并展示用于建立形状对应的应用程序。 我们提出许多有趣的和有意义的形状变化的例子,即使从最少的3-10个培训形状开始,我们也提出了许多有趣的和有意义的形状变化的例子。