Face Generation
69 papers with code • 0 benchmarks • 2 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
iFakeFaceDB
VGGFace2 HQ
Most implemented papers
Progressive Growing of GANs for Improved Quality, Stability, and Variation
tkarras/progressive_growing_of_gans
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ICLR 2018
We describe a new training methodology for generative adversarial networks.
Everybody Dance Now
carolineec/EverybodyDanceNow
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ICCV 2019
This paper presents a simple method for "do as I do" motion transfer: given a source video of a person dancing, we can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves.
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
We propose a novel attributes encoder for extracting multi-level target face attributes, and a new generator with carefully designed Adaptive Attentional Denormalization (AAD) layers to adaptively integrate the identity and the attributes for face synthesis.
Learning a model of facial shape and expression from 4D scans
Rubikplayer/flame-fitting
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SIGGRAPH Asia 2017
FLAME is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model.
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
eladrich/pixel2style2pixel
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CVPR 2021
We present a generic image-to-image translation framework, pixel2style2pixel (pSp).
GANimation: Anatomically-aware Facial Animation from a Single Image
albertpumarola/GANimation
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ECCV 2018
Recent advances in Generative Adversarial Networks (GANs) have shown impressive results for task of facial expression synthesis.
Interpreting the Latent Space of GANs for Semantic Face Editing
ShenYujun/InterFaceGAN
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CVPR 2020
In this work, we propose a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs.
Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation
minivision-ai/photo2cartoon
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In this paper, we are interested in generating fine-grained cartoon faces for various groups.
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.
