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.

本文提出了一种使用对比学习和隐式姿态嵌入的 3D 感知生成对抗网络优化技术。通过重新设计鉴别器，从而消除对基准相机姿态的依赖性，使其更好地捕捉给定图像的隐式姿态嵌入，并在姿态嵌入上进行对比学习，实验结果表明在多个物体类别和不一致的基准相机姿态数据集上显著优于现有方法。