论文标题
Instantavatar：在60秒内从单眼视频中学习化身
InstantAvatar: Learning Avatars from Monocular Video in 60 Seconds
论文作者
论文摘要
在本文中，我们通过贡献Instantavatar来迈出了一步，朝着单眼神经头像重建的现实适用性迈出了一步，该系统可以在几秒钟内从单眼视频中重建人类化身，并且可以在几秒钟内从单眼视频中重建人类化身，并且可以以交互式速率进行动画和渲染。为了达到这一效率，我们提出了一个经过精心设计和设计的系统，该系统利用新兴的加速结构来为神经场，并结合有效的动态场景的有效的空式空格铲策策略。我们还贡献了一个有效的实施，我们将用于研究目的。与现有方法相比，Instantavatar的收敛速度更快，可以在几分钟而不是数小时内进行培训。它实现了可比甚至更好的重建质量和新型姿势合成结果。当给定同一时间预算时，我们的方法大大优于SOTA方法。 Instantavatar可以在短短10秒钟的训练时间内产生可接受的视觉质量。
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.
