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Dongjin Huang

2 accepted papers

2022

Learning To Restore 3D Face From In-the-Wild Degraded Images

CVPR 2022poster

In-the-wild 3D face modelling is a challenging problem as the predicted facial geometry and texture suffer from a lack of reliable clues or priors, when the input images are degraded. To address such a problem, in this paper we propose a novel Learning to Restore (L2R) 3D face framework for unsuperv…

Cited by 3PDFScholar
2022

Physically-Guided Disentangled Implicit Rendering for 3D Face Modeling

CVPR 2022poster

This paper presents a novel Physically-guided Disentangled Implicit Rendering (PhyDIR) framework for high-fidelity 3D face modeling. The motivation comes from two observations: widely-used graphics renderers yield excessive approximations against photo-realistic imaging, while neural rendering metho…

Cited by 8PDFScholar