Face Generation
Face generation is the task of generating (or interpolating) new faces from an existing dataset.
The state-of-the-art results for this task are located in the Image Generation parent.
( Image credit: Progressive Growing of GANs for Improved Quality, Stability, and Variation )
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|
Subtasks
Greatest papers with code
Progressive Growing of GANs for Improved Quality, Stability, and Variation
ICLR 2018
• tkarras/progressive_growing_of_gans
• 
We describe a new training methodology for generative adversarial networks.
#2 best model for
Image Generation
on CIFAR-10
GANimation: Anatomically-aware Facial Animation from a Single Image
ECCV 2018
• albertpumarola/GANimation
• 
Recent advances in Generative Adversarial Networks (GANs) have shown impressive results for task of facial expression synthesis.
CONDITIONAL IMAGE GENERATION FACE GENERATION IMAGE-TO-IMAGE TRANSLATION
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
• Hangz-nju-cuhk/Talking-Face-Generation-DAVS
•
Talking face generation aims to synthesize a sequence of face images that correspond to a clip of speech.
Photo-Realistic Facial Details Synthesis from Single Image
ICCV 2019
• apchenstu/Facial_Details_Synthesis
•
Our technique employs expression analysis for proxy face geometry generation and combines supervised and unsupervised learning for facial detail synthesis.
Interpreting the Latent Space of GANs for Semantic Face Editing
In this work, we propose a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs.
Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition
Benchmarking our model on one of the most popular unconstrained face recognition datasets IJB-C additionally verifies the promising generalizability of AIM in recognizing faces in the wild.
AGE-INVARIANT FACE RECOGNITION FACE GENERATION REPRESENTATION LEARNING
Face Synthesis for Eyeglass-Robust Face Recognition
A feasible method is to collect large-scale face images with eyeglasses for training deep learning methods.
COCO-GAN: Generation by Parts via Conditional Coordinating
ICCV 2019
• hubert0527/COCO-GAN
•
Despite the full images are never generated during training, we show that COCO-GAN can produce \textbf{state-of-the-art-quality} full images during inference.
Learning a model of facial shape and expression from 4D scans
SIGGRAPH Asia 2017
• Rubikplayer/flame-fitting
•
FLAME is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model.
Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model
ECCV 2018
• barisgecer/facegan
•
We propose a novel end-to-end semi-supervised adversarial framework to generate photorealistic face images of new identities with wide ranges of expressions, poses, and illuminations conditioned by a 3D morphable model.
DOMAIN ADAPTATION FACE GENERATION FACE RECOGNITION STYLE TRANSFER