Face Generation
120 papers with code • 0 benchmarks • 4 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Face Generation
Libraries
Use these libraries to find Face Generation models and implementationsSubtasks
Most implemented papers
Domain-Specific Face Synthesis for Video Face Recognition from a Single Sample Per Person
A compact set of synthetic faces is generated that resemble individuals of interest under the capture conditions relevant to the OD.
Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model
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.
Talking Face Generation by Conditional Recurrent Adversarial Network
Given an arbitrary face image and an arbitrary speech clip, the proposed work attempts to generating the talking face video with accurate lip synchronization while maintaining smooth transition of both lip and facial movement over the entire video clip.
Face Synthesis for Eyeglass-Robust Face Recognition
A feasible method is to collect large-scale face images with eyeglasses for training deep learning methods.
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
Talking face generation aims to synthesize a sequence of face images that correspond to a clip of speech.
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.
Triple consistency loss for pairing distributions in GAN-based face synthesis
To show this is effective, we incorporate the triple consistency loss into the training of a new landmark-guided face to face synthesis, where, contrary to previous works, the generated images can simultaneously undergo a large transformation in both expression and pose.
RankGAN: A Maximum Margin Ranking GAN for Generating Faces
We present a new stage-wise learning paradigm for training generative adversarial networks (GANs).
Photo-Realistic Facial Details Synthesis from Single Image
Our technique employs expression analysis for proxy face geometry generation and combines supervised and unsupervised learning for facial detail synthesis.
COCO-GAN: Generation by Parts via Conditional Coordinating
On the computation side, COCO-GAN has a built-in divide-and-conquer paradigm that reduces memory requisition during training and inference, provides high-parallelism, and can generate parts of images on-demand.