One2Avatar: Generative Implicit Head Avatar For Few-shot User Adaptation
Zhixuan Yu Ziqian Bai Abhimitra Meka Feitong Tan Qiangeng Xu Rohit Pandey Sean Fanello Hyun Soo Park Yinda Zhang
Traditional methods for constructing high-quality, personalized head avatars
from monocular videos demand extensive face captures and training time, posing
a significant challenge for scalability. This paper introduces a novel approach
to create high quality head avatar utilizing only a single or a few images per
user. We learn a generative model for 3D animatable photo-realistic head avatar
from a multi-view dataset of expressions from 2407 subjects, and leverage it as
a prior for creating personalized avatar from few-shot images. Different from
previous 3D-aware face generative models, our prior is built with a
3DMM-anchored neural radiance field backbone, which we show to be more
effective for avatar creation through auto-decoding based on few-shot inputs.
We also handle unstable 3DMM fitting by jointly optimizing the 3DMM fitting and
camera calibration that leads to better few-shot adaptation. Our method
demonstrates compelling results and outperforms existing state-of-the-art
methods for few-shot avatar adaptation, paving the way for more efficient and
personalized avatar creation.