One2Avatar：用于少样本用户适应的生成式隐式头部虚拟人
One2Avatar: Generative Implicit Head Avatar For Few-shot User Adaptation
AI总结：
本文提出One2Avatar，一种基于3DMM锚定神经辐射场骨干网络的生成式隐式头部虚拟人方法，通过联合优化3DMM拟合与相机标定，仅用单张或少量图像即可实现高效且高质量的个性化少样本用户适应。
摘要：
传统从单目视频构建高质量个性化头部虚拟人的方法需要大量的人脸捕捉数据和训练时间，对可扩展性提出了重大挑战。本文提出了一种新方法，仅使用每个用户的单张或少量图像即可创建高质量头部虚拟人。我们从包含2407个受试者表情的多视图数据集中学习了一个用于3D可动画化照片级真实头部虚拟人的生成模型，并将其作为先验，从少样本图像中创建个性化虚拟人。与以往的3D感知人脸生成模型不同，我们的先验模型建立在以3DMM为锚点的神经辐射场骨干网络上，我们证明该骨干网络通过基于少样本输入的自解码过程，在虚拟人创建中更为有效。我们还通过联合优化3DMM拟合和相机标定来处理不稳定的3DMM拟合问题，从而实现更好的少样本适应。我们的方法展示了极具说服力的结果，并在少样本虚拟人适应方面优于现有的最先进方法，为更高效、更个性化的虚拟人创建铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
