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
FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction
Our pipeline utilizes the recent advances in StyleGAN-based facial image editing approaches to generate multi-view normalized face images from single-image inputs.
Perspective Reconstruction of Human Faces by Joint Mesh and Landmark Regression
Even though 3D face reconstruction has achieved impressive progress, most orthogonal projection-based face reconstruction methods can not achieve accurate and consistent reconstruction results when the face is very close to the camera due to the distortion under the perspective projection.
FDNeRF: Few-shot Dynamic Neural Radiance Fields for Face Reconstruction and Expression Editing
fdnerf/fdnerf
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Unlike existing dynamic NeRFs that require dense images as input and can only be modeled for a single identity, our method enables face reconstruction across different persons with few-shot inputs.
Visual Speech-Aware Perceptual 3D Facial Expression Reconstruction from Videos
filby89/spectre
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The recent state of the art on monocular 3D face reconstruction from image data has made some impressive advancements, thanks to the advent of Deep Learning.
KeypointNeRF: Generalizing Image-based Volumetric Avatars using Relative Spatial Encoding of Keypoints
facebookresearch/KeypointNeRF
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In this work, we investigate common issues with existing spatial encodings and propose a simple yet highly effective approach to modeling high-fidelity volumetric humans from sparse views.
Towards 3D Face Reconstruction in Perspective Projection: Estimating 6DoF Face Pose from Monocular Image
In 3D face reconstruction, orthogonal projection has been widely employed to substitute perspective projection to simplify the fitting process.
EMOCA: Emotion Driven Monocular Face Capture and Animation
While EMOCA achieves 3D reconstruction errors that are on par with the current best methods, it significantly outperforms them in terms of the quality of the reconstructed expression and the perceived emotional content.
Towards Metrical Reconstruction of Human Faces
Zielon/MICA
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To this end, we take advantage of a face recognition network pretrained on a large-scale 2D image dataset, which provides distinct features for different faces and is robust to expression, illumination, and camera changes.
From 2D Images to 3D Model:Weakly Supervised Multi-View Face Reconstruction with Deep Fusion
While weakly supervised multi-view face reconstruction (MVR) is garnering increased attention, one critical issue still remains open: how to effectively fuse multiple image information to reconstruct high-precision 3D models.
FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset
lizhenwangt/faceverse
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CVPR 2022
In the coarse module, we generate a base parametric model from large-scale RGB-D images, which is able to predict accurate rough 3D face models in different genders, ages, etc.
