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arXiv · 2402.11909

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
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Abstract

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.

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Zhixuan Yu, Ziqian Bai, Abhimitra Meka, Feitong Tan, Qiangeng Xu, Rohit Pandey, Sean Fanello, Hyun Soo Park, Yinda Zhang. 2024-02-19. One2Avatar: Generative Implicit Head Avatar For Few-shot User Adaptation. https://arxiv.org/abs/2402.11909

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