We present InstantGeoAvatar, a method for efficient and effective learning from monocular video of detailed 3D geometry and appearance of animatable implicit human avatars. Our key observation is that the optimization of a hash grid encoding to represent a signed distance function (SDF) of the human subject is fraught with instabilities and bad local minima. We thus propose a principled geometry-aware SDF regularization scheme that seamlessly fits into the volume rendering pipeline and adds negligible computational overhead. Our regularization scheme significantly outperforms previous approaches for training SDFs on hash grids. We obtain competitive results in geometry reconstruction and novel view synthesis in as little as five minutes of training time, a significant reduction from the several hours required by previous work. InstantGeoAvatar represents a significant leap forward towards achieving interactive reconstruction of virtual avatars.

本研究针对从单目视频中提取可动画人类头像的3D几何和外观的困难进行了探讨。我们提出了一种基于几何的SDF正则化方案，显著提高了在哈希网格上训练SDF的效率和稳定性。研究表明，该方法能在短短五分钟内实现良好的几何重建和新视图合成，标志着虚拟头像交互重建的重大进展。

即时几何头像：从单目视频中有效建模可动画头像的几何和外观