  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.
