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In this work we introduce Lifting Autoencoders, a generative 3D surface-basedmodel of object categories. We bring together ideas from non-rigid structurefrom motion, image formation, and morphable models to learn a controllable,geometric model of 3D categories in an entirely unsupervised manner from anunstructured set of images. We exploit the 3D geometric nature of our model anduse normal information to disentangle appearance into illumination, shading andalbedo. We further use weak supervision to disentangle the non-rigid shapevariability of human faces into identity and expression. We combine the 3Drepresentation with a differentiable renderer to generate RGB images and appendan adversarially trained refinement network to obtain sharp, photorealisticimage reconstruction results. The learned generative model can be controlled interms of interpretable geometry and appearance factors, allowing us to performphotorealistic image manipulation of identity, expression, 3D pose, andillumination properties.

Lifting AutoEncoders: Unsupervised Learning of a Fully-Disentangled 3D Morphable Model using Deep Non-Rigid Structure from Motion