  Recent work in Neural Fields (NFs) learn 3D representations from
class-specific single view image collections. However, they are unable to
reconstruct the input data preserving high-frequency details. Further, these
methods do not disentangle appearance from geometry and hence are not suitable
for tasks such as texture transfer and editing. In this work, we propose TEGLO
(Textured EG3D-GLO) for learning 3D representations from single view
in-the-wild image collections for a given class of objects. We accomplish this
by training a conditional Neural Radiance Field (NeRF) without any explicit 3D
supervision. We equip our method with editing capabilities by creating a dense
correspondence mapping to a 2D canonical space. We demonstrate that such
mapping enables texture transfer and texture editing without requiring meshes
with shared topology. Our key insight is that by mapping the input image pixels
onto the texture space we can achieve near perfect reconstruction (>= 74 dB
PSNR at 1024^2 resolution). Our formulation allows for high quality 3D
consistent novel view synthesis with high-frequency details at megapixel image
resolution.
