Fueled by the power of deep learning techniques and implicit shape learning,
recent advances in single-image human digitalization have reached unprecedented
accuracy and could recover fine-grained surface details such as garment
wrinkles. However, a common problem for the implicit-based methods is that they
cannot produce separated and topology-consistent mesh for each garment piece,
which is crucial for the current 3D content creation pipeline. To address this
issue, we proposed a novel geometry inference framework ReEF that reconstructs
topology-consistent layered garment mesh by registering the explicit garment
template to the whole-body implicit fields predicted from single images.
Experiments demonstrate that our method notably outperforms its counterparts on
single-image layered garment reconstruction and could bring high-quality
digital assets for further content creation.

利用深度学习技术和隐含形状学习的力量，提出一种新颖的几何推断框架 ReEF，通过将显式模板与单张图片生成的整体隐式场进行匹配，重建具有拓扑一致性的分层服装网格，实验表明我们的方法在单张图像重建方面优于同类方法并能够为进一步的内容创作提供高质量的数字资源