Drawing upon StyleGAN's expressivity and disentangled latent space, existing
2D approaches employ textual prompting to edit facial images with different
attributes. In contrast, 3D-aware approaches that generate faces at different
target poses require attribute-specific classifiers, learning separate model
weights for each attribute, and are not scalable for novel attributes. In this
work, we propose an efficient, plug-and-play, 3D-aware face editing framework
based on attribute-specific prompt learning, enabling the generation of facial
images with controllable attributes across various target poses. To this end,
we introduce a text-driven learnable style token-based latent attribute editor
(LAE). The LAE harnesses a pre-trained vision-language model to find
text-guided attribute-specific editing direction in the latent space of any
pre-trained 3D-aware GAN. It utilizes learnable style tokens and style mappers
to learn and transform this editing direction to 3D latent space. To train LAE
with multiple attributes, we use directional contrastive loss and style token
loss. Furthermore, to ensure view consistency and identity preservation across
different poses and attributes, we employ several 3D-aware identity and pose
preservation losses. Our experiments show that our proposed framework generates
high-quality images with 3D awareness and view consistency while maintaining
attribute-specific features. We demonstrate the effectiveness of our method on
different facial attributes, including hair color and style, expression, and
others. Code:
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提出了一种基于属性特定的提示学习的高效、即插即用的三维感知人脸编辑框架，该框架利用可学习的样式令牌和样式映射器，通过预先训练的视觉语言模型在任何预先训练的三维感知生成对抗网络的潜在空间中找到以文本为导向的属性特定编辑方向，并将其转化为三维潜在空间。通过多个三维感知的身份和姿势保持损失，保证了视角一致性和身份保持。实验证明，该方法能够生成具有三维感知和视角一致性的高质量图像，同时保持属性特定的特征。