  Occlusions are a common occurrence in unconstrained face images. Single image
3D reconstruction from such face images often suffers from corruption due to
the presence of occlusions. Furthermore, while a plurality of 3D
reconstructions is plausible in the occluded regions, existing approaches are
limited to generating only a single solution. To address both of these
challenges, we present Diverse3DFace, which is specifically designed to
simultaneously generate a diverse and realistic set of 3D reconstructions from
a single occluded face image. It consists of three components: a global+local
shape fitting process, a graph neural network-based mesh VAE, and a
Determinantal Point Process based diversity promoting iterative optimization
procedure. Quantitative and qualitative comparisons of 3D reconstruction on
occluded faces show that Diverse3DFace can estimate 3D shapes that are
consistent with the visible regions in the target image while exhibiting high,
yet realistic, levels of diversity on the occluded regions. On face images
occluded by masks, glasses, and other random objects, Diverse3DFace generates a
distribution of 3D shapes having ~50% higher diversity on the occluded regions
compared to the baselines. Moreover, our closest sample to the ground truth has
~40% lower MSE than the singular reconstructions by existing approaches.
