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This paper presents a method to reconstruct high-quality textured 3D modelsfrom single images. Current methods rely on datasets with expensiveannotations; multi-view images and their camera parameters. Our method relieson GAN generated multi-view image datasets which have a negligible annotationcost. However, they are not strictly multi-view consistent and sometimes GANsoutput distorted images. This results in degraded reconstruction qualities. Inthis work, to overcome these limitations of generated datasets, we have twomain contributions which lead us to achieve state-of-the-art results onchallenging objects: 1) A robust multi-stage learning scheme that graduallyrelies more on the models own predictions when calculating losses, 2) A noveladversarial learning pipeline with online pseudo-ground truth generations toachieve fine details. Our work provides a bridge from 2D supervisions of GANmodels to 3D reconstruction models and removes the expensive annotationefforts. We show significant improvements over previous methods whether theywere trained on GAN generated multi-view images or on real images withexpensive annotations. Please visit our web-page for 3D visuals:https://research.nvidia.com/labs/adlr/progressive-3d-learning

Progressive Learning of 3D Reconstruction Network from 2D GAN Data