  Collecting accurate camera poses of training images has been shown to well
serve the learning of 3D-aware generative adversarial networks (GANs) yet can
be quite expensive in practice. This work targets learning 3D-aware GANs from
unposed images, for which we propose to perform on-the-fly pose estimation of
training images with a learned template feature field (TeFF). Concretely, in
addition to a generative radiance field as in previous approaches, we ask the
generator to also learn a field from 2D semantic features while sharing the
density from the radiance field. Such a framework allows us to acquire a
canonical 3D feature template leveraging the dataset mean discovered by the
generative model, and further efficiently estimate the pose parameters on real
data. Experimental results on various challenging datasets demonstrate the
superiority of our approach over state-of-the-art alternatives from both the
qualitative and the quantitative perspectives.
