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

Learning 3D-aware Image Synthesis with Unknown Pose Distribution