3D Face Reconstruction
76 papers with code • 7 benchmarks • 11 datasets
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.
( Image credit: 3DDFA_V2 )
Benchmarks
These leaderboards are used to track progress in 3D Face Reconstruction
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Libraries
Use these libraries to find 3D Face Reconstruction models and implementationsDatasets
AFLW2000-3D
FaceWarehouse
FaceScape
Florence
ExPose
NoW Benchmark
REALY
FFHQ-UV
DAD-3DHeads
FaMoS
Latest papers with no code
A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization
We propose NeuFace, a 3D face mesh pseudo annotation method on videos via neural re-parameterized optimization.
3D Face Reconstruction: the Road to Forensics
Therefore, it provides an analysis of the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discusses the current obstacles that separate 3D face reconstruction from an active role in forensic applications.
Angle Range and Identity Similarity Enhanced Gaze and Head Redirection based on Synthetic data
In this paper, we propose a method for improving the angular accuracy and photo-reality of gaze and head redirection in full-face images.
Disjoint Pose and Shape for 3D Face Reconstruction
Existing methods for 3D face reconstruction from a few casually captured images employ deep learning based models along with a 3D Morphable Model(3DMM) as face geometry prior.
NeutrEx: A 3D Quality Component Measure on Facial Expression Neutrality
Accurate face recognition systems are increasingly important in sensitive applications like border control or migration management.
FLAME-based Multi-View 3D Face Reconstruction
Our work mainly addresses the problem of combining parametric models of faces with multi-view face 3D reconstruction and explores the implementation of a Flame based multi-view training and testing framework for contributing to the field of face 3D reconstruction.
Rethinking Voice-Face Correlation: A Geometry View
Previous works on voice-face matching and voice-guided face synthesis demonstrate strong correlations between voice and face, but mainly rely on coarse semantic cues such as gender, age, and emotion.
Fake It Without Making It: Conditioned Face Generation for Accurate 3D Face Reconstruction
Our synthesis method conditions Stable Diffusion on depth maps sampled from the FLAME 3D Morphable Model (3DMM) of the human face, allowing us to generate a diverse set of shape-consistent facial images that is designed to be balanced in race and gender.
FaceCLIPNeRF: Text-driven 3D Face Manipulation using Deformable Neural Radiance Fields
To do so, we first train a scene manipulator, a latent code-conditional deformable NeRF, over a dynamic scene to control a face deformation using the latent code.
Facial Landmark Detection Evaluation on MOBIO Database
Our work is first to perform facial landmark detection evaluation on the mobile still data, i. e., face images from MOBIO database.
