  Existing methods for 3D-aware image synthesis largely depend on the 3D pose
distribution pre-estimated on the training set. An inaccurate estimation may
mislead the model into learning faulty geometry. This work proposes PoF3D that
frees generative radiance fields from the requirements of 3D pose priors. We
first equip the generator with an efficient pose learner, which is able to
infer a pose from a latent code, to approximate the underlying true pose
distribution automatically. We then assign the discriminator a task to learn
pose distribution under the supervision of the generator and to differentiate
real and synthesized images with the predicted pose as the condition. The
pose-free generator and the pose-aware discriminator are jointly trained in an
adversarial manner. Extensive results on a couple of datasets confirm that the
performance of our approach, regarding both image quality and geometry quality,
is on par with state of the art. To our best knowledge, PoF3D demonstrates the
feasibility of learning high-quality 3D-aware image synthesis without using 3D
pose priors for the first time.
