The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently, Neural Radiance Field (NeRF) has been introduced for synthesizing novel views with low computational cost and superior performance. While several works investigate a generative NeRF and show remarkable achievement, they cannot handle conditional and continuous feature manipulation in the generation procedure. In this work, we introduce a novel model, called Class-Continuous Conditional Generative NeRF ($\text{C}^{3}$G-NeRF), which can synthesize conditionally manipulated photorealistic 3D-consistent images by projecting conditional features to the generator and the discriminator. The proposed $\text{C}^{3}$G-NeRF is evaluated with three image datasets, AFHQ, CelebA, and Cars. As a result, our model shows strong 3D-consistency with fine details and smooth interpolation in conditional feature manipulation. For instance, $\text{C}^{3}$G-NeRF exhibits a Fr\'echet Inception Distance (FID) of 7.64 in 3D-aware face image synthesis with a $\text{128}^{2}$ resolution. Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with $\text{C}^{3}$G-NeRF.

翻译：3D感知图像合成在生成高分辨率细节图像的同时，还需保持空间一致性。近年来，神经辐射场（NeRF）因其低计算成本与卓越性能被引入新视角合成领域。尽管已有研究探索了生成式NeRF并取得显著成果，但这些方法无法在生成过程中实现条件化与连续特征操控。本文提出一种新型模型——类连续条件生成式NeRF（$\text{C}^{3}$G-NeRF），通过将条件特征投影至生成器与判别器，可合成条件操控下的逼真3D一致图像。该模型在AFHQ、CelebA和Cars三个图像数据集上进行了评估。实验结果表明，$\text{C}^{3}$G-NeRF在条件特征操控中展现出强3D一致性、精细细节与平滑插值能力。例如，在$\text{128}^{2}$分辨率下的3D感知人脸图像合成任务中，其Fréchet初始距离（FID）达到7.64。此外，由于$\text{C}^{3}$G-NeRF支持类条件图像合成，我们还提供了数据集中每类生成图像的FID值。