We present a unified framework tackling two problems: class-specific 3D
reconstruction from a single image, and generation of new 3D shape samples.
These tasks have received considerable attention recently; however, existing
approaches rely on 3D supervision, annotation of 2D images with keypoints or
poses, and/or training with multiple views of each object instance. Our
framework is very general: it can be trained in similar settings to these
existing approaches, while also supporting weaker supervision scenarios.
Importantly, it can be trained purely from 2D images, without ground-truth pose
annotations, and with a single view per instance. We employ meshes as an output
representation, instead of voxels used in most prior work. This allows us to
exploit shading information during training, which previous 2D-supervised
methods cannot. Thus, our method can learn to generate and reconstruct concave
object classes. We evaluate our approach on synthetic data in various settings,
showing that (i) it learns to disentangle shape from pose; (ii) using shading
in the loss improves performance; (iii) our model is comparable or superior to
state-of-the-art voxel-based approaches on quantitative metrics, while
producing results that are visually more pleasing; (iv) it still performs well
when given supervision weaker than in prior works.

论文提出了一种统一框架，用于解决单幅图像特定类别的 3D 重建和新 3D 形状生成的问题。该方法支持弱监督学习，只需要单个实例的 2D 图像，使用网格作为输出表示，并利用光照信息提高了性能。实验结果表明，该方法在定量度量上与最新的基于体素方法相当或优于，而且结果更加美观，并且在弱监督学习方面表现良好。