In this work we introduce Deforming Autoencoders, a generative model for
images that disentangles shape from appearance in an unsupervised manner. As in
the deformable template paradigm, shape is represented as a deformation between
a canonical coordinate system (`template') and an observed image, while
appearance is modeled in `canonical', template, coordinates, thus discarding
variability due to deformations. We introduce novel techniques that allow this
approach to be deployed in the setting of autoencoders and show that this
method can be used for unsupervised group-wise image alignment. We show
experiments with expression morphing in humans, hands, and digits, face
manipulation, such as shape and appearance interpolation, as well as
unsupervised landmark localization. A more powerful form of unsupervised
disentangling becomes possible in template coordinates, allowing us to
successfully decompose face images into shading and albedo, and further
manipulate face images.

Deforming Autoencoders 是一种用于图像生成的生成模型，通过无监督方式将形状与外观分离。使用模板坐标系表示形状，并且可以成功分解面部图像成明暗和反照率两部分，在模板坐标系上更强大的无监督分解变得可能。