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Occlusions are a common occurrence in unconstrained face images. Single image3D reconstruction from such face images often suffers from corruption due tothe presence of occlusions. Furthermore, while a plurality of 3Dreconstructions is plausible in the occluded regions, existing approaches arelimited to generating only a single solution. To address both of thesechallenges, we present Diverse3DFace, which is specifically designed tosimultaneously generate a diverse and realistic set of 3D reconstructions froma single occluded face image. It consists of three components: a global+localshape fitting process, a graph neural network-based mesh VAE, and aDeterminantal Point Process based diversity promoting iterative optimizationprocedure. Quantitative and qualitative comparisons of 3D reconstruction onoccluded faces show that Diverse3DFace can estimate 3D shapes that areconsistent with the visible regions in the target image while exhibiting high,yet realistic, levels of diversity on the occluded regions. On face imagesoccluded by masks, glasses, and other random objects, Diverse3DFace generates adistribution of 3D shapes having ~50% higher diversity on the occluded regionscompared to the baselines. Moreover, our closest sample to the ground truth has~40% lower MSE than the singular reconstructions by existing approaches.

Generating Diverse 3D Reconstructions from a Single Occluded Face Image