We propose FlashAvatar, a novel and lightweight 3D animatable avatar representation that could reconstruct a digital avatar from a short monocular video sequence in minutes and render high-fidelity photo-realistic images at 300FPS on a consumer-grade GPU. To achieve this, we maintain a uniform 3D Gaussian field embedded in the surface of a parametric face model and learn extra spatial offset to model non-surface regions and subtle facial details. While full use of geometric priors can capture high-frequency facial details and preserve exaggerated expressions, proper initialization can help reduce the number of Gaussians, thus enabling super-fast rendering speed. Extensive experimental results demonstrate that FlashAvatar outperforms existing works regarding visual quality and personalized details and is almost an order of magnitude faster in rendering speed. Project page: https://ustc3dv.github.io/FlashAvatar/

翻译：我们提出FlashAvatar，一种新颖且轻量级的3D可动画化身表示方法，能够从单目视频序列中在数分钟内重建数字化身，并在消费级GPU上以300FPS的速度渲染高保真照片级真实图像。为实现此目标，我们维护了一个嵌入参数化人脸模型表面的均匀3D高斯场，并学习额外空间偏移以建模非表面区域及细微面部细节。充分利用几何先验可捕捉高频面部细节并保留夸张表情，而适当的初始化有助于减少高斯数量，从而实现超快渲染速度。大量实验结果表明，FlashAvatar在视觉质量与个性化细节上优于现有方法，且在渲染速度上几乎提升了一个数量级。项目页面：https://ustc3dv.github.io/FlashAvatar/