Hi3DFace: High-Realistic 3D Face Reconstruction From a Single Occluded Image
| dc.contributor.author | Huang, Dongjin | en_US |
| dc.contributor.author | Shi, Yongsheng | en_US |
| dc.contributor.author | Qu, Jiantao | en_US |
| dc.contributor.author | Liu, Jinhua | en_US |
| dc.contributor.author | Tang, Wen | en_US |
| dc.contributor.editor | Wimmer, Michael | en_US |
| dc.contributor.editor | Alliez, Pierre | en_US |
| dc.contributor.editor | Westermann, RĂĽdiger | en_US |
| dc.date.accessioned | 2025-11-07T08:33:32Z | |
| dc.date.available | 2025-11-07T08:33:32Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | We propose Hi3DFace, a novel framework for simultaneous de-occlusion and high-fidelity 3D face reconstruction. To address real-world occlusions, we construct a diverse facial dataset by simulating common obstructions and present TMANet, a transformer-based multi-scale attention network that effectively removes occlusions and restores clean face images. For the 3D face reconstruction stage, we propose a coarse-medium-fine self-supervised scheme. In the coarse reconstruction pipeline, we adopt a face regression network to predict 3DMM coefficients for generating a smooth 3D face. In the medium-scale reconstruction pipeline, we propose a novel depth displacement network, DDFTNet, to remove noise and restore rich details to the smooth 3D geometry. In the fine-scale reconstruction pipeline, we design a GCN (graph convolutional network) refiner to enhance the fidelity of 3D textures. Additionally, a light-aware network (LightNet) is proposed to distil lighting parameters, ensuring illumination consistency between reconstructed 3D faces and input images. Extensive experimental results demonstrate that the proposed Hi3DFace significantly outperforms state-of-the-art reconstruction methods on four public datasets, and five constructed occlusion-type datasets. Hi3DFace achieves robustness and effectiveness in removing occlusions and reconstructing 3D faces from real-world occluded facial images. | en_US |
| dc.description.number | 6 | |
| dc.description.sectionheaders | Original Article | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 44 | |
| dc.identifier.doi | 10.1111/cgf.70277 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 14 pages | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.70277 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.1111/cgf70277 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | modelling | |
| dc.subject | appearance modelling | |
| dc.subject | facial modelling | |
| dc.subject | geometric modelling | |
| dc.subject | Computing methodologies→Texturing | |
| dc.subject | Reflectance modelling | |
| dc.subject | Shape modelling | |
| dc.title | Hi3DFace: High-Realistic 3D Face Reconstruction From a Single Occluded Image | en_US |
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