One2Avatar: Generative Implicit Head Avatar For Few-shot User Adaptation
<|reference_start|>One2Avatar: Generative Implicit Head Avatar For Few-shot User Adaptation: 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.<|reference_end|>
@article{yu2024one2avatar:,
    title={One2Avatar: Generative Implicit Head Avatar For Few-shot User Adaptation},
    author={Zhixuan Yu, Ziqian Bai, Abhimitra Meka, Feitong Tan, Qiangeng Xu,
  Rohit Pandey, Sean Fanello, Hyun Soo Park and Yinda Zhang},
    journal={arXiv preprint arXiv:2402.11909},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11909},
    primaryClass={cs.CV}
}