In this paper, we take a significant step towards real-world applicability of monocular neural avatar reconstruction by contributing InstantAvatar, a system that can reconstruct human avatars from a monocular video within seconds, and these avatars can be animated and rendered at an interactive rate. To achieve this efficiency we propose a carefully designed and engineered system, that leverages emerging acceleration structures for neural fields, in combination with an efficient empty space-skipping strategy for dynamic scenes. We also contribute an efficient implementation that we will make available for research purposes. Compared to existing methods, InstantAvatar converges 130x faster and can be trained in minutes instead of hours. It achieves comparable or even better reconstruction quality and novel pose synthesis results. When given the same time budget, our method significantly outperforms SoTA methods. InstantAvatar can yield acceptable visual quality in as little as 10 seconds training time.
翻译:在本文中,我们迈出了重大一步,通过贡献 " InstantAvatar ",实现单心神经元重建的现实应用。 " InstatAvatar " 是一个可以在数秒内从单心视频中重建人类血管的系统,这个系统可以在数秒内从单心血管视频中重建人类血管,这些血管可以动画,以交互速度完成。为了实现这一效率,我们提出了一个精心设计和设计的系统,利用神经场正在形成的加速结构,同时为动态场景制定有效的空降空间战略。我们还促进高效的实施,我们将为研究目的提供。与现有方法相比, " InstantAvatar " 更快地将130x连接起来,可以在几分钟而不是几个小时内接受培训。它实现了可比的甚至更好的重建质量,并提出了新的合成结果。在同样预算下,我们的方法大大优于 SoTA方法。 " InstastAvatar " 可以在10秒的培训时间内产生可接受的视觉质量。