  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.
