  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.
