Domain adaptation of 3D portraits has gained more and more attention. However, the transfer mechanism of existing methods is mainly based on vision or language, which ignores the potential of vision-language combined guidance. In this paper, we propose an Image-Text multi-modal framework, namely Image and Text portrait (ITportrait), for 3D portrait domain adaptation. ITportrait relies on a two-stage alternating training strategy. In the first stage, we employ a 3D Artistic Paired Transfer (APT) method for image-guided style transfer. APT constructs paired photo-realistic portraits to obtain accurate artistic poses, which helps ITportrait to achieve high-quality 3D style transfer. In the second stage, we propose a 3D Image-Text Embedding (ITE) approach in the CLIP space. ITE uses a threshold function to self-adaptively control the optimization direction of images or texts in the CLIP space. Comprehensive experiments prove that our ITportrait achieves state-of-the-art (SOTA) results and benefits downstream tasks. All source codes and pre-trained models will be released to the public.

翻译：三维肖像的域自适应已受到越来越多的关注。然而，现有方法的迁移机制主要基于视觉或语言，忽略了视觉-语言联合指导的潜力。本文提出一种图像-文本多模态框架，即图像与文本肖像（ITportrait），用于三维肖像域自适应。ITportrait依赖于一种两阶段交替训练策略。在第一阶段，我们采用三维艺术配对迁移（APT）方法实现图像引导的风格迁移。APT通过构建配对的逼真肖像来获取精确的艺术姿态，从而帮助ITportrait实现高质量的三维风格迁移。在第二阶段，我们提出一种在CLIP空间中的三维图像-文本嵌入（ITE）方法。ITE利用阈值函数自适应地控制CLIP空间中图像或文本的优化方向。全面的实验证明，我们的ITportrait取得了最先进（SOTA）的结果，并有益于下游任务。所有源代码与预训练模型将公开发布。