Face Generation
118 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 implementations
iFakeFaceDB
DFDM
VGGFace2 HQ
Face dataset by Generated Photos
Subtasks
Most implemented papers
Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks
Speech is a rich biometric signal that contains information about the identity, gender and emotional state of the speaker.
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
Specifically, a new generator architecture is proposed to simultaneously transfer color/texture styles and transform local facial shapes into anime-like counterparts based on the style of a reference anime-face, while preserving the global structure of the source photo-face.
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
onion-liu/BlendGAN
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NeurIPS 2021
Specifically, we first train a self-supervised style encoder on the generic artistic dataset to extract the representations of arbitrary styles.
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
mkocaoglu/CausalGAN
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ICLR 2018
We show that adversarial training can be used to learn a generative model with true observational and interventional distributions if the generator architecture is consistent with the given causal graph.
GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
DetionDX/GP-GAN-Gender-Preserving-GAN-for-Synthesizing-Faces-from-Landmarks
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The primary aim of this work is to demonstrate that information preserved by landmarks (gender in particular) can be further accentuated by leveraging generative models to synthesize corresponding faces.
Unsupervised Face Normalization With Extreme Pose and Expression in the Wild
Face normalization provides an effective and cheap way to distil face identity and dispel face variances for recognition.
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
lissomx/MSP
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ICML 2020
We demonstrate the utility of our method for attribute manipulation in autoencoders trained across varied domains, using both human evaluation and automated methods.
On the Detection of Digital Face Manipulation
JStehouwer/FFD_CVPR2020
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CVPR 2020
Instead of simply using multi-task learning to simultaneously detect manipulated images and predict the manipulated mask (regions), we propose to utilize an attention mechanism to process and improve the feature maps for the classification task.
GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection
The availability of large-scale facial databases, together with the remarkable progresses of deep learning technologies, in particular Generative Adversarial Networks (GANs), have led to the generation of extremely realistic fake facial content, raising obvious concerns about the potential for misuse.
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
The free access to large-scale public databases, together with the fast progress of deep learning techniques, in particular Generative Adversarial Networks, have led to the generation of very realistic fake content with its corresponding implications towards society in this era of fake news.
