We present TimeWalker, a novel framework that models realistic, full-scale 3D head avatars of a person on lifelong scale. Unlike current human head avatar pipelines that capture identity at the momentary level(e.g., instant photography or short videos), TimeWalker constructs a person's comprehensive identity from unstructured data collection over his/her various life stages, offering a paradigm to achieve full reconstruction and animation of that person at different moments of life. At the heart of TimeWalker's success is a novel neural parametric model that learns personalized representation with the disentanglement of shape, expression, and appearance across ages. Central to our methodology are the concepts of two aspects: (1) We track back to the principle of modeling a person's identity in an additive combination of average head representation in the canonical space, and moment-specific head attribute representations driven from a set of neural head basis. To learn the set of head basis that could represent the comprehensive head variations in a compact manner, we propose a Dynamic Neural Basis-Blending Module (Dynamo). It dynamically adjusts the number and blend weights of neural head bases, according to both shared and specific traits of the target person over ages. (2) Dynamic 2D Gaussian Splatting (DNA-2DGS), an extension of Gaussian splatting representation, to model head motion deformations like facial expressions without losing the realism of rendering and reconstruction. DNA-2DGS includes a set of controllable 2D oriented planar Gaussian disks that utilize the priors from parametric model, and move/rotate with the change of expression. Through extensive experimental evaluations, we show TimeWalker's ability to reconstruct and animate avatars across decoupled dimensions with realistic rendering effects, demonstrating a way to achieve personalized 'time traveling' in a breeze.

翻译：我们提出TimeWalker，一种新颖的框架，用于在终身时间尺度上对人物真实、全尺寸的3D头部虚拟形象进行建模。与当前在瞬时层面（例如即时摄影或短视频）捕捉身份的人类头部虚拟形象流程不同，TimeWalker从个体在不同生命阶段的无结构化数据收集中构建其全面的身份表征，为实现该个体在生命不同时刻的完整重建与动画提供了一种范式。TimeWalker成功的关键在于一种新颖的神经参数化模型，该模型学习跨年龄的、解耦了形状、表情与外观的个性化表征。我们方法的核心包含两个方面的概念：(1) 我们回溯到在规范空间中，将个人身份建模为平均头部表征与由一组神经头部基驱动的、特定时刻头部属性表征的加性组合这一基本原理。为了学习能以紧凑方式表示全面头部变化的一组头部基，我们提出了动态神经基混合模块（Dynamo）。它根据目标人物跨年龄的共享与特定特征，动态调整神经头部基的数量与混合权重。(2) 动态2D高斯泼溅（DNA-2DGS），这是高斯泼溅表示的一种扩展，用于建模头部运动变形（如面部表情），同时不损失渲染与重建的真实感。DNA-2DGS包含一组可控的2D定向平面高斯圆盘，它们利用来自参数化模型的先验，并随表情变化而移动/旋转。通过广泛的实验评估，我们展示了TimeWalker在解耦维度上重建和动画虚拟形象的能力，并具有逼真的渲染效果，为轻松实现个性化的"时间旅行"展示了一种途径。