One2Avatar: Generative Implicit Head Avatar For Few-shot User Adaptation

• URL: http://arxiv.org/abs/2402.11909v1
• Date: Mon, 19 Feb 2024 07:48:29 GMT
• Title: One2Avatar: Generative Implicit Head Avatar For Few-shot User Adaptation
• Authors: Zhixuan Yu, Ziqian Bai, Abhimitra Meka, Feitong Tan, Qiangeng Xu, Rohit Pandey, Sean Fanello, Hyun Soo Park and Yinda Zhang
• Abstract summary: 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. Our method demonstrates compelling results and outperforms existing state-of-the-art methods for few-shot avatar adaptation.
• Score: 31.310769289315648
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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