2D GANs Meet Unsupervised Single-view 3D Reconstruction

• URL: http://arxiv.org/abs/2207.10183v1
• Date: Wed, 20 Jul 2022 20:24:07 GMT
• Title: 2D GANs Meet Unsupervised Single-view 3D Reconstruction
• Authors: Feng Liu, Xiaoming Liu
• Abstract summary: controllable image generation based on pre-trained GANs can benefit a wide range of computer vision tasks. We propose a novel image-conditioned neural implicit field, which can leverage 2D supervisions from GAN-generated multi-view images. The effectiveness of our approach is demonstrated through superior single-view 3D reconstruction results of generic objects.
• Score: 21.93671761497348
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent research has shown that controllable image generation based on pre-trained GANs can benefit a wide range of computer vision tasks. However, less attention has been devoted to 3D vision tasks. In light of this, we propose a novel image-conditioned neural implicit field, which can leverage 2D supervisions from GAN-generated multi-view images and perform the single-view reconstruction of generic objects. Firstly, a novel offline StyleGAN-based generator is presented to generate plausible pseudo images with full control over the viewpoint. Then, we propose to utilize a neural implicit function, along with a differentiable renderer to learn 3D geometry from pseudo images with object masks and rough pose initializations. To further detect the unreliable supervisions, we introduce a novel uncertainty module to predict uncertainty maps, which remedy the negative effect of uncertain regions in pseudo images, leading to a better reconstruction performance. The effectiveness of our approach is demonstrated through superior single-view 3D reconstruction results of generic objects.

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