We present a learning framework for recovering the 3D shape, camera, and texture of an object from a single image. The shape is represented as a deformable 3D mesh model of an object category where a shape is parameterized by a learned mean shape and per-instance predicted deformation. Our approach allows leveraging an annotated image collection for training, where the deformable model and the 3D prediction mechanism are learned without relying on ground-truth 3D or multi-view supervision. Our representation enables us to go beyond existing 3D prediction approaches by incorporating texture inference as prediction of an image in a canonical appearance space. Additionally, we show that semantic keypoints can be easily associated with the predicted shapes. We present qualitative and quantitative results of our approach on the CUB dataset, and show that we can learn to predict the diverse shapes and textures across birds using only an annotated image collection. We also demonstrate the the applicability of our method for learning the 3D structure of other generic categories.

本文提出一种学习框架，可以从单个图像中恢复物体的3D形状、相机、和纹理，使用一个可变形的3D网格模型来表示形状，在不依赖于地面真值3D或多视图监督的情况下，利用注释的图像集进行训练并学习3D预测机制与可变形模型。作者还展示了可以轻松将语义关键点与预测的形状相关联，并在CUB和PASCAL3D数据集上展示了我们的方法的定性和定量结果。

从图像集合学习类别特定的网格重建