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.
The code and model will be made available upon publication.
