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.

Abstract Existing high‐resolution face‐swapping works are still challenges in preserving identity consistency while maintaining high visual quality. We present a novel high‐resolution face‐swapping method GPSwap, which is based on StyleGAN prior. To better preserves identity consistency, the proposed facial feature recombination network fully leverages the properties of both w space and encoders to decouple identities. Furthermore, we presents the image reconstruction module aligns and blends images in FS space, which further supplements facial details and achieves natural blending. It not only improves image resolution but also optimizes visual quality. Extensive experiments and user studies demonstrate that GPSwap is superior to state‐of‐the‐art high‐resolution face‐swapping methods in terms of image quality and identity consistency. In addition, GPSwap saves nearly 80% of training costs compared to other high‐resolution face‐swapping works.