This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a
model that learns an interpretable representation of images. This
representation is disentangled with respect to transformations such as
out-of-plane rotations and lighting variations. The DC-IGN model is composed of
multiple layers of convolution and de-convolution operators and is trained
using the Stochastic Gradient Variational Bayes (SGVB) algorithm. We propose a
training procedure to encourage neurons in the graphics code layer to represent
a specific transformation (e.g. pose or light). Given a single input image, our
model can generate new images of the same object with variations in pose and
lighting. We present qualitative and quantitative results of the model's
efficacy at learning a 3D rendering engine.

本文提出了 Deep Convolution Inverse Graphics Network（DC-IGN）模型，用于学习图像的可解释表示，该表示与平面外旋转和光照变化等变换无关，模型采用多层卷积和反卷积操作，并使用随机梯度变分贝叶斯算法进行训练，我们提出了一种训练程序，以鼓励图形代码层的神经元表示特定的变换（例如姿势或光照）。给定一个单一的输入图像，我们的模型可以生成同一对象的新图像，其中包含姿态和光照的变化，本文提供了模型学习 3D 渲染引擎的定性和定量结果。