AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation
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/.
648fd3f860488151a13cae54
Yifei Zeng
648fd3f860488151a13cae55
Yuanxun Lu
648fd3f860488151a13cae56
Xinya Ji
648fd3f860488151a13cae57
Yao Yao
648fd3f860488151a13cae58
Hao Zhu
648fd3f860488151a13cae59
Xun Cao
AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation
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/.
AvatarBooth generates high-quality 3D avatars from text prompts or images using dual fine-tuned diffusion models and a multi-resolution rendering strategy, achieving superior rendering and geometric quality.
diffusion models pose-consistent constraint multi-resolution rendering strategy 3D avatar generation