  Although 3D-aware GANs based on neural radiance fields have achieved
competitive performance, their applicability is still limited to objects or
scenes with the ground-truths or prediction models for clearly defined
canonical camera poses. To extend the scope of applicable datasets, we propose
a novel 3D-aware GAN optimization technique through contrastive learning with
implicit pose embeddings. To this end, we first revise the discriminator design
and remove dependency on ground-truth camera poses. Then, to capture complex
and challenging 3D scene structures more effectively, we make the discriminator
estimate a high-dimensional implicit pose embedding from a given image and
perform contrastive learning on the pose embedding. The proposed approach can
be employed for the dataset, where the canonical camera pose is ill-defined
because it does not look up or estimate camera poses. Experimental results show
that our algorithm outperforms existing methods by large margins on the
datasets with multiple object categories and inconsistent canonical camera
poses.
