The manipulation of latent space has recently become an interesting topic in
the field of generative models. Recent research shows that latent directions
can be used to manipulate images towards certain attributes. However,
controlling the generation process of 3D generative models remains a challenge.
In this work, we propose a novel 3D manipulation method that can manipulate
both the shape and texture of the model using text or image-based prompts such
as 'a young face' or 'a surprised face'. We leverage the power of Contrastive
Language-Image Pre-training (CLIP) model and a pre-trained 3D GAN model
designed to generate face avatars, and create a fully differentiable rendering
pipeline to manipulate meshes. More specifically, our method takes an input
latent code and modifies it such that the target attribute specified by a text
or image prompt is present or enhanced, while leaving other attributes largely
unaffected. Our method requires only 5 minutes per manipulation, and we
demonstrate the effectiveness of our approach with extensive results and
comparisons.

该论文介绍了一种基于文本或图像的提示（如 “年轻的脸” 或 “惊讶的脸”）来操纵三维生成模型中形态和纹理的方法，利用了对比语言图像预训练模型（CLIP）和预训练的生成人脸的三维 GAN 模型创建了一个完全可微的渲染管道来操作网格。