Data of general object images have two most common structures: (1) each
object of a given shape can be rendered in multiple different views, and (2)
shapes of objects can be categorized in such a way that the diversity of shapes
is much larger across categories than within a category. Existing deep
generative models can typically capture either structure, but not both. In this
work, we introduce a novel deep generative model, called CIGMO, that can learn
to represent category, shape, and view factors from image data. The model is
comprised of multiple modules of shape representations that are each
specialized to a particular category and disentangled from view representation,
and can be learned using a group-based weakly supervised learning method. By
empirical investigation, we show that our model can effectively discover
categories of object shapes despite large view variation and quantitatively
supersede various previous methods including the state-of-the-art invariant
clustering algorithm. Further, we show that our approach using
category-specialization can enhance the learned shape representation to better
perform down-stream tasks such as one-shot object identification as well as
shape-view disentanglement.

本文提出了一种名为 CIGMO 的新型深度生成模型，可以从图像数据中学习表示类别、形状和视图等因素。通过实验证明，该模型可以有效地发现物体形状的类别，即使视角差异很大，并且比以前的方法更好地完成了诸如单次物体识别和形状 - 视图解缠的任务。

CIGMO: 在深度生成框架中的分类不变表示

CIGMO: Categorical invariant representations in a deep generative framework

3D hand pose estimation from single depth is a fundamental problem in
computer vision, and has wide applications.However, the existing methods still
can not achieve satisfactory hand pose estimation results due to view variation
and occlusion of human hand. In this paper, we propose a new virtual view
selection and fusion module for 3D hand pose estimation from single depth.We
propose to automatically select multiple virtual viewpoints for pose estimation
and fuse the results of all and find this empirically delivers accurate and
robust pose estimation. In order to select most effective virtual views for
pose fusion, we evaluate the virtual views based on the confidence of virtual
views using a light-weight network via network distillation. Experiments on
three main benchmark datasets including NYU, ICVL and Hands2019 demonstrate
that our method outperforms the state-of-the-arts on NYU and ICVL, and achieves
very competitive performance on Hands2019-Task1, and our proposed virtual view
selection and fusion module is both effective for 3D hand pose estimation.

本文提出了一种从单一深度中进行 3D 手势姿势估计的新方法，包括虚拟视角选择和融合模块，有效解决了视角变化和手部遮挡等问题，并在三大基准数据集上表现出优越性能。