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 this https URL

通过观察只有少数示例的关节物体数据集，我们提出了一种解决少样本关节网格生成问题的层次网格变形生成模型，通过从大规模刚性网格中借用可转移的变形模式来缓解少样本挑战，并通过物理感知的变形修正方案鼓励产生物理上合理的生成物。在 6 个关节类别上进行了广泛实验，证明了我们的方法在少样本环境中生成具有更好的多样性、更高的视觉保真度和更好的物理有效性的关节网格方面的优势。

基于分层变形的小样本物理感知关节网格生成

Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical  Deformation

Generative adversarial networks (GAN) have recently been used for a design
synthesis of mechanical shapes. A GAN sometimes outputs physically unreasonable
shapes. For example, when a GAN model is trained to output airfoil shapes that
indicate required aerodynamic performance, significant errors occur in the
performance values. This is because the GAN model only considers data but does
not consider the aerodynamic equations that lie under the data. This paper
proposes the physics-guided training of the GAN model to guide the model to
learn physical validity. Physical validity is computed using general-purpose
software located outside the neural network model. Such general-purpose
software cannot be used in physics-informed neural network frameworks, because
physical equations must be implemented inside the neural network models.
Additionally, a limitation of generative models is that the output data are
similar to the training data and cannot generate completely new shapes.
However, because the proposed model is guided by a physical model and does not
use a training dataset, it can generate completely new shapes. Numerical
experiments show that the proposed model drastically improves the accuracy.
Moreover, the output shapes differ from those of the training dataset but still
satisfy the physical validity, overcoming the limitations of existing GAN
models.

通过物理引导训练生成对抗网络（GAN）模型，在不使用训练数据集的情况下，生成全新的形状，并且改善了现有 GAN 模型的准确性和物理有效性。