LightHeadEd：基于智能手机的可重新打光且可编辑的头部虚拟化身
LightHeadEd: Relightable & Editable Head Avatars from a Smartphone
AI总结：
针对传统3D头部虚拟化身需昂贵设备的问题，提出仅用带偏振滤镜的智能手机创建可重新打光头部虚拟化身的方法，通过偏振视频分离反射成分、混合表示保留细节，还收集了多受试者数据集。
摘要：
传统上，创建逼真、可动画化且可重新打光的3D头部虚拟化身需要昂贵的Lightstage系统及多台校准相机，这限制了其广泛应用。为解决这一问题，我们提出一种新颖、经济高效的方法，仅使用配备偏振滤镜的智能手机即可创建高质量可重新打光的头部虚拟化身。我们的方法包括在暗室中使用单点光源同时捕捉交叉偏振和平行偏振视频流，分离动态面部表现中的皮肤漫反射和镜面反射成分。我们引入一种混合表示，在参数化头部模型的UV空间中嵌入2D高斯函数，在保留高保真几何细节的同时实现高效实时渲染。我们基于学习的神经分析合成管线将依赖于姿态和表情的几何偏移与外观解耦，将表面分解为反照率、法线和镜面反射UV纹理图以及环境图。我们收集了一个独特的数据集，包含不同受试者执行多种面部表情和头部动作的内容。
英文摘要：
Creating photorealistic, animatable, and relightable 3D head avatars traditionally requires expensive Lightstage with multiple calibrated cameras, making it inaccessible for widespread adoption. To bridge this gap, we present a novel, cost-effective approach for creating high-quality relightable head avatars using only a smartphone equipped with polaroid filters. Our approach involves simultaneously capturing cross-polarized and parallel-polarized video streams in a dark room with a single point-light source, separating the skin's diffuse and specular components during dynamic facial performances. We introduce a hybrid representation that embeds 2D Gaussians in the UV space of a parametric head model, facilitating efficient real-time rendering while preserving high-fidelity geometric details. Our learning-based neural analysis-by-synthesis pipeline decouples pose and expression-dependent geometrical offsets from appearance, decomposing the surface into albedo, normal, and specular UV texture maps, along with the environment maps. We collect a unique dataset of various subjects performing diverse facial expressions and head movements.
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。
