Generative Neural Radiance Fields (GNeRF) based 3D-aware GANs have demonstrated remarkable capabilities in generating high-quality images while maintaining strong 3D consistency. Notably, significant advancements have been made in the domain of face generation. However, most existing models prioritize view consistency over disentanglement, resulting in limited semantic/attribute control during generation. To address this limitation, we propose a conditional GNeRF model incorporating specific attribute labels as input to enhance the controllability and disentanglement abilities of 3D-aware generative models. Our approach builds upon a pre-trained 3D-aware face model, and we introduce a Training as Init and Optimizing for Tuning (TRIOT) method to train a conditional normalized flow module to enable the facial attribute editing, then optimize the latent vector to improve attribute-editing precision further. Our extensive experiments demonstrate that our model produces high-quality edits with superior view consistency while preserving non-target regions. Code is available at https://github.com/zhangqianhui/TT-GNeRF.

翻译：基于生成式神经辐射场（GNeRF）的三维感知生成对抗网络在生成高质量图像的同时，展现出卓越的三维一致性能力。尤其在面部生成领域取得了显著进展。然而，现有模型大多优先考虑视角一致性而非解耦性，导致生成过程中的语义/属性控制能力受限。为解决这一局限，我们提出一种条件式GNeRF模型，通过引入特定属性标签作为输入，增强三维感知生成模型的可控性与解耦能力。我们的方法基于预训练的三维感知人脸模型，提出"初始训练与调优优化"（TRIOT）方法：首先训练条件归一化流模块实现面部属性编辑，然后优化潜在向量以进一步提升属性编辑精度。大量实验表明，该模型在保持非目标区域不变的同时，能够生成具有优异视角一致性的高质量编辑结果。代码已开源：https://github.com/zhangqianhui/TT-GNeRF。