Registering Explicit to Implicit: Towards High-Fidelity Garment mesh Reconstruction from Single Images
Heming Zhu, Lingteng Qiu, Yuda Qiu, Xiaoguang Han
Abstract
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 the counterparts on single-image layered garment reconstruction and could bring high-quality digital assets for further content creation.