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:我们展示了瞬间重建光现实数字变异器的新颖方法(INSTA),即即瞬间重建光现实数字变异器的新方法。INSTA以嵌入一个准度面观模型的神经图形原始体为基础,模拟一个动态神经光亮场。我们的输油管是用单一的单镜 RGB 肖像视频培训的,该视频以不同的表达和观点观察该主题。尽管最先进的方法需要几天的时间来训练一个变异器,但我们的方法可以在不到10分钟的时间内在现代GPU硬件上重建一个数字变异器,这比以前的解决方案要快得多。此外,它还允许交互式地展示新的外观和表达方式。通过利用基本准度面观模型之前的几何测量方法,我们证明INSTA在各种主题的定量和定性研究中,INSTA在质量和培训时间方面超越了最先进的方法。