We present Instant Volumetric Head Avatars (INSTA), a novel approach for reconstructing photo-realistic digital avatars instantaneously. INSTA models a dynamic neural radiance field based on neural graphics primitives embedded around a parametric face model. Our pipeline is trained on a single monocular RGB portrait video that observes the subject under different expressions and views. While state-of-the-art methods take up to several days to train an avatar, our method can reconstruct a digital avatar in less than 10 minutes on modern GPU hardware, which is orders of magnitude faster than previous solutions. In addition, it allows for the interactive rendering of novel poses and expressions. By leveraging the geometry prior of the underlying parametric face model, we demonstrate that INSTA extrapolates to unseen poses. In quantitative and qualitative studies on various subjects, INSTA outperforms state-of-the-art methods regarding rendering quality and training time.

翻译：我们提出即时体积头部虚拟形象（INSTA），一种用于即时重建逼真数字虚拟形象的新方法。INSTA基于嵌入参数化面部模型周围的神经图形基元，建模动态神经辐射场。我们的流程通过单一单目RGB肖像视频进行训练，该视频在不同表情和视角下捕捉主体。尽管现有最先进方法需要多达数天时间训练虚拟形象，但我们的方法在现代GPU硬件上可在不到10分钟内重建数字虚拟形象，速度比先前解决方案快数个数量级。此外，该方法支持实时渲染新姿态和表情。通过利用底层参数化面部模型的几何先验，我们证明INSTA可外推到未见姿态。在多个主体的定量与定性研究中，INSTA在渲染质量和训练时间方面均优于最先进方法。