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
REALY: Rethinking the Evaluation of 3D Face Reconstruction
The evaluation of 3D face reconstruction results typically relies on a rigid shape alignment between the estimated 3D model and the ground-truth scan.
Cross-Modal Perceptionist: Can Face Geometry be Gleaned from Voices?
choyingw/Voice2Mesh
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CVPR 2022
This work digs into a root question in human perception: can face geometry be gleaned from one's voices?
Non-Deterministic Face Mask Removal Based On 3D Priors
This paper presents a novel image inpainting framework for face mask removal.
Learning-by-Novel-View-Synthesis for Full-Face Appearance-Based 3D Gaze Estimation
Despite recent advances in appearance-based gaze estimation techniques, the need for training data that covers the target head pose and gaze distribution remains a crucial challenge for practical deployment.
HairMapper: Removing Hair From Portraits Using GANs
onethousand1000/non-hair-ffhq
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CVPR 2022
Removing hair from portrait images is challenging due to the complex occlusions between hair and face, as well as the lack of paired portrait data with/without hair.
Self-Supervised Robustifying Guidance for Monocular 3D Face Reconstruction
Therefore, we propose a Self-Supervised RObustifying GUidancE (ROGUE) framework to obtain robustness against occlusions and noise in the face images.
AvatarMe++: Facial Shape and BRDF Inference with Photorealistic Rendering-Aware GANs
Nevertheless, to the best of our knowledge, there is no method which can produce render-ready high-resolution 3D faces from "in-the-wild" images and this can be attributed to the: (a) scarcity of available data for training, and (b) lack of robust methodologies that can successfully be applied on very high-resolution data.
Self-supervised Re-renderable Facial Albedo Reconstruction from Single Image
To further make facial textures disentangled with illumination, we propose a novel detailed illumination representation which is reconstructed with the detailed albedo together.
FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction
By training on FaceScape data, a novel algorithm is proposed to predict elaborate riggable 3D face models from a single image input.
Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry
Our synergy process leverages a representation cycle for 3DMM parameters and 3D landmarks.
