论文标题
即时体积头像
Instant Volumetric Head Avatars
论文作者
论文摘要
我们提出了即时的体积头像（Insta），这是一种新型的方法，用于立即重建照片现实的数字化头像。 Insta基于围绕参数面模型嵌入的神经图形原语的动态神经辐射场模型。我们的管道经过单眼RGB肖像视频进行了训练，该视频以不同的表达和观点观察了主题。虽然最新的方法需要长达几天的时间来训练头像，但我们的方法可以在不到10分钟的现代GPU硬件上重建数字化身，这比以前的解决方案快。此外，它允许对新型姿势和表达的互动渲染。通过利用潜在参数面模型的几何形状，我们证明了Insta外推到看不见的姿势。在有关各种科目的定量和定性研究中，Insta优于呈现质量和培训时间的最先进方法。
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.
