Face Reconstruction
75 papers with code • 0 benchmarks • 3 datasets
Face reconstruction is the task of recovering the facial geometry of a face from an image.
( Image credit: Microsoft Deep3DFaceReconstruction )
Benchmarks
These leaderboards are used to track progress in Face Reconstruction
FaceWarehouse
FaceScape
ICT-3DHP
Most implemented papers
SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination
jesse1029/SiGAN
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Despite generative adversarial networks (GANs) can hallucinate photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they cannot guarantee preserving the identities of hallucinated HR faces, making the HR faces poorly recognizable.
3D Face From X: Learning Face Shape from Diverse Sources
Although 3D scanned data contain accurate geometric information of face shapes, the capture system is expensive and such datasets usually contain a small number of subjects.
MobileFace: 3D Face Reconstruction with Efficient CNN Regression
Estimation of facial shapes plays a central role for face transfer and animation.
3D Face Modeling From Diverse Raw Scan Data
Traditional 3D face models learn a latent representation of faces using linear subspaces from limited scans of a single database.
GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
In this paper, we take a radically different approach and harness the power of Generative Adversarial Networks (GANs) and DCNNs in order to reconstruct the facial texture and shape from single images.
Dual Attention MobDenseNet(DAMDNet) for Robust 3D Face Alignment
3D face alignment of monocular images is a crucial process in the recognition of faces with disguise. 3D face reconstruction facilitated by alignment can restore the face structure which is helpful in detcting disguise interference. This paper proposes a dual attention mechanism and an efficient end-to-end 3D face alignment framework. We build a stable network model through Depthwise Separable Convolution, Densely Connected Convolutional and Lightweight Channel Attention Mechanism.
Unsupervised Eyeglasses Removal in the Wild
Given two facial images with and without eyeglasses, the proposed model learns to swap the eye area in two faces.
DF2Net: A Dense-Fine-Finer Network for Detailed 3D Face Reconstruction
xiaoxingzeng/DF2Net
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ICCV 2019
In addition, we introduce three types of data to train these networks, including 3D model synthetic data, 2D image reconstructed data, and fine facial images.
Face Manifold: Manifold Learning for Synthetic Face Generation
The main challenge of such techniques is a vital need for large 3D face datasets.
Self-supervised Learning of Detailed 3D Face Reconstruction
cyj907/unsupervised-detail-layer
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The displacement map and the coarse model are used to render a final detailed face, which again can be compared with the original input image to serve as a photometric loss for the second stage.
