We introduce AvatarBooth, a novel method for generating high-quality 3D avatars using text prompts or specific images. Unlike previous approaches that can only synthesize avatars based on simple text descriptions, our method enables the creation of personalized avatars from casually captured face or body images, while still supporting text-based model generation and editing. Our key contribution is the precise avatar generation control by using dual fine-tuned diffusion models separately for the human face and body. This enables us to capture intricate details of facial appearance, clothing, and accessories, resulting in highly realistic avatar generations. Furthermore, we introduce pose-consistent constraint to the optimization process to enhance the multi-view consistency of synthesized head images from the diffusion model and thus eliminate interference from uncontrolled human poses. In addition, we present a multi-resolution rendering strategy that facilitates coarse-to-fine supervision of 3D avatar generation, thereby enhancing the performance of the proposed system. The resulting avatar model can be further edited using additional text descriptions and driven by motion sequences. Experiments show that AvatarBooth outperforms previous text-to-3D methods in terms of rendering and geometric quality from either text prompts or specific images. Please check our project website at https://zeng-yifei.github.io/avatarbooth_page/.

翻译：我们提出AvatarBooth方法，一种通过文本提示或特定图像生成高质量三维头像的新方法。与先前仅能基于简单文本描述合成头像的方法不同，我们的方法支持从随意拍摄的面部或身体图像创建个性化头像，同时仍保留基于文本的模型生成与编辑能力。我们的核心贡献在于利用分别针对人脸和人体进行双参数微调的扩散模型，实现对头像生成的精准控制。这使得我们能够捕捉面部外观、服装和配饰的精细细节，从而生成高度逼真的头像。此外，我们在优化过程中引入姿态一致性约束，增强扩散模型合成头部图像的多视角一致性，从而消除不可控人体姿态的干扰。同时，我们提出多分辨率渲染策略，通过由粗到细的监督方式促进三维头像生成，提升系统性能。生成的头像模型还可通过额外文本描述进行编辑，并支持运动序列驱动。实验表明，无论基于文本提示还是特定图像，AvatarBooth在渲染质量与几何质量上均优于先前的文本到三维方法。详情请访问项目网站：https://zeng-yifei.github.io/avatarbooth_page/。