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Although 3D-aware GANs based on neural radiance fields have achievedcompetitive performance, their applicability is still limited to objects orscenes with the ground-truths or prediction models for clearly definedcanonical camera poses. To extend the scope of applicable datasets, we proposea novel 3D-aware GAN optimization technique through contrastive learning withimplicit pose embeddings. To this end, we first revise the discriminator designand remove dependency on ground-truth camera poses. Then, to capture complexand challenging 3D scene structures more effectively, we make the discriminatorestimate a high-dimensional implicit pose embedding from a given image andperform contrastive learning on the pose embedding. The proposed approach canbe employed for the dataset, where the canonical camera pose is ill-definedbecause it does not look up or estimate camera poses. Experimental results showthat our algorithm outperforms existing methods by large margins on thedatasets with multiple object categories and inconsistent canonical cameraposes.

ContraNeRF: 3D-Aware Generative Model via Contrastive Learning with Unsupervised Implicit Pose Embedding