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 this https URL

AvatarBooth 是一种新方法，通过使用文本提示或特定图像生成高质量的 3D 头像，可以从捕捉的面部或身体图像中创建个性化头像，其利用双精度扩散模型控制头部和身体生成，增强了合成头部图像的多视图一致性，实现了高度逼真的头像生成，具有出色的渲染和几何质量。