We study the problem of few-shot physically-aware articulated mesh generation. By observing an articulated object dataset containing only a few examples, we wish to learn a model that can generate diverse meshes with high visual fidelity and physical validity. Previous mesh generative models either have difficulties in depicting a diverse data space from only a few examples or fail to ensure physical validity of their samples. Regarding the above challenges, we propose two key innovations, including 1) a hierarchical mesh deformation-based generative model based upon the divide-and-conquer philosophy to alleviate the few-shot challenge by borrowing transferrable deformation patterns from large scale rigid meshes and 2) a physics-aware deformation correction scheme to encourage physically plausible generations. We conduct extensive experiments on 6 articulated categories to demonstrate the superiority of our method in generating articulated meshes with better diversity, higher visual fidelity, and better physical validity over previous methods in the few-shot setting. Further, we validate solid contributions of our two innovations in the ablation study. Project page with code is available at https://meowuu7.github.io/few-arti-obj-gen.

翻译：我们研究少样本物理感知铰接网格生成问题。通过观察仅包含少量示例的铰接物体数据集，我们希望学习一个能够生成具有高视觉保真度和物理有效性的多样化网格的模型。以往的网格生成模型要么难以从少量示例中刻画多样化的数据空间，要么无法确保样本的物理有效性。针对上述挑战，我们提出两项关键创新，包括：1) 基于分治思想的层级网格形变生成模型，通过从大规模刚体网格中借鉴可迁移形变模式来缓解少样本挑战；2) 物理感知形变校正方案，以鼓励生成物理合理的形变。我们在6个铰接类别上进行了广泛实验，证明我们的方法在少样本情境下生成的铰接网格在多样性、视觉保真度和物理有效性方面均优于以往方法。此外，消融实验验证了两项创新的实质性贡献。包含代码的项目页面请访问 https://meowuu7.github.io/few-arti-obj-gen。