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3D Face Reconstruction
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3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Reconstructing A Large Scale 3D Face Dataset for Deep 3D Face Identification
Learning 3D Face Reconstruction with a Pose Guidance Network
A Human Ear Reconstruction Autoencoder
Learning Complete 3D Morphable Face Models from Images and Videos
Towards Fast, Accurate and Stable 3D Dense Face Alignment
Pixel-Face: A Large-Scale, High-Resolution Benchmark for 3D Face Reconstruction
Monocular Expressive Body Regression through Body-Driven Attention
Fast and Robust Face-to-Parameter Translation for Game Character Auto-Creation
Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency
Personalized Face Modeling for Improved Face Reconstruction and Motion Retargeting
Adaptive 3D Face Reconstruction from a Single Image
Multi-spectral Facial Landmark Detection
TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition
Landmark Detection and 3D Face Reconstruction for Caricature using a Nonlinear Parametric Model
AvatarMe: Realistically Renderable 3D Facial Reconstruction "in-the-wild"
Lightweight Photometric Stereo for Facial Details Recovery
Cross-modal Deep Face Normals with Deactivable Skip Connections
Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks
Everybody's Talkin': Let Me Talk as You Want
Joint Face Super-Resolution and Deblurring Using a Generative Adversarial Network
Self-supervised Learning of Detailed 3D Face Reconstruction
Face Manifold: Manifold Learning for Synthetic Face Generation
Dual Attention MobDenseNet(DAMDNet) for Robust 3D Face Alignment
Learning Robust 3D Face Reconstruction and Discriminative Identity Representation
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
Face De-occlusion using 3D Morphable Model and Generative Adversarial Network
3D Dense Face Alignment via Graph Convolution Networks
Towards High-fidelity Nonlinear 3D Face Morphable Model
3D Face Reconstruction Using Color Photometric Stereo with Uncalibrated Near Point Lights
FEAFA: A Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation
3D Face Reconstruction from A Single Image Assisted by 2D Face Images in the Wild
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set
Joint Face Detection and Facial Motion Retargeting for Multiple Faces
GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
3D Face Modeling From Diverse Raw Scan Data
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