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Face Reconstruction

Face reconstruction is the task of recovering the facial geometry of a face from an image.

( Image credit: Microsoft Deep3DFaceReconstruction )

Papers

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Showing 201–225 of 259 papers

TitleStatusHype
An Adversarial Neuro-Tensorial Approach For Learning Disentangled Representations—0
Angle Range and Identity Similarity Enhanced Gaze and Head Redirection based on Synthetic data—0
A Perceptual Shape Loss for Monocular 3D Face Reconstruction—0
A Self-Supervised Bootstrap Method for Single-Image 3D Face Reconstruction—0
ASM: Adaptive Skinning Model for High-Quality 3D Face Modeling—0
Attention based Occlusion Removal for Hybrid Telepresence Systems—0
Audio-Driven 3D Facial Animation from In-the-Wild Videos—0
Automated 3D Face Reconstruction From Multiple Images Using Quality Measures—0
AVFace: Towards Detailed Audio-Visual 4D Face Reconstruction—0
BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs—0
BareSkinNet: De-makeup and De-lighting via 3D Face Reconstruction—0
B-spline Shape from Motion & Shading: An Automatic Free-form Surface Modeling for Face Reconstruction—0
CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images—0
Consistent and symmetry preserving data-driven interface reconstruction for the level-set method—0
Continuous Landmark Detection With 3D Queries—0
Deception Detection by 2D-to-3D Face Reconstruction from Videos—0
Discriminative Deep Feature Visualization for Explainable Face Recognition—0
Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition—0
Disjoint Pose and Shape for 3D Face Reconstruction—0
DNPM: A Neural Parametric Model for the Synthesis of Facial Geometric Details—0
Does Visual Self-Supervision Improve Learning of Speech Representations for Emotion Recognition?—0
Dual Codebook VQ: Enhanced Image Reconstruction with Reduced Codebook Size—0
Dual Encoder-Decoder based Generative Adversarial Networks for Disentangled Facial Representation Learning—0
End-to-end 3D face reconstruction with deep neural networks—0
End to End Face Reconstruction via Differentiable PnP—0
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