Registering Explicit to Implicit: Towards High-Fidelity Garment Mesh Reconstruction From Single Images

Heming Zhu, Lingteng Qiu, Yuda Qiu, Xiaoguang Han; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 3845-3854

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.

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[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Zhu_2022_CVPR, author = {Zhu, Heming and Qiu, Lingteng and Qiu, Yuda and Han, Xiaoguang}, title = {Registering Explicit to Implicit: Towards High-Fidelity Garment Mesh Reconstruction From Single Images}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {3845-3854} }