MoSAR: Monocular Semi-Supervised Model for Avatar Reconstruction Using Differentiable Shading

Abstract

Reconstructing an avatar from a portrait image has many applications in multimedia but remains a challenging research problem. Extracting reflectance maps and geometry from one image is ill-posed: recovering geometry is a one-to-many mapping problem and reflectance and light are difficult to disentangle. Accurate geometry and reflectance can be captured under the controlled conditions of a light stage but it is costly to acquire large datasets in this fashion. Moreover training solely with this type of data leads to poor generalization with in-the-wild images. This motivates the introduction of MoSAR a method for 3D avatar generation from monocular images. We propose a semi-supervised training scheme that improves generalization by learning from both light stage and in-the-wild datasets. This is achieved using a novel differentiable shading formulation. We show that our approach effectively disentangles the intrinsic face parameters producing relightable avatars. As a result MoSAR estimates a richer set of skin reflectance maps and generates more realistic avatars than existing state-of-the-art methods. We also release a new dataset that provides intrinsic face attributes (diffuse specular ambient occlusion and translucency maps) for 10k subjects.