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 implementations
iFakeFaceDB
DFDM
VGGFace2 HQ
Face dataset by Generated Photos
Subtasks
Latest papers
Cross-Age Contrastive Learning for Age-Invariant Face Recognition
Cross-age facial images are typically challenging and expensive to collect, making noise-free age-oriented datasets relatively small compared to widely-used large-scale facial datasets.
Neural Text to Articulate Talk: Deep Text to Audiovisual Speech Synthesis achieving both Auditory and Photo-realism
g-milis/NEUTART
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Our method, which we call NEUral Text to ARticulate Talk (NEUTART), is a talking face generator that uses a joint audiovisual feature space, as well as speech-informed 3D facial reconstructions and a lip-reading loss for visual supervision.
HyperLips: Hyper Control Lips with High Resolution Decoder for Talking Face Generation
First, FaceEncoder is used to obtain latent code by extracting features from the visual face information taken from the video source containing the face frame. Then, HyperConv, which weighting parameters are updated by HyperNet with the audio features as input, will modify the latent code to synchronize the lip movement with the audio.
HDTR-Net: A Real-Time High-Definition Teeth Restoration Network for Arbitrary Talking Face Generation Methods
In particular, we propose a Fine-Grained Feature Fusion (FGFF) module to effectively capture fine texture feature information around teeth and surrounding regions, and use these features to fine-grain the feature map to enhance the clarity of teeth.
Limitations of Face Image Generation
wi-pi/limitations_of_face_generation
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In particular, their ability to synthesize and modify human faces has spurred research into using generated face images in both training data augmentation and model performance assessments.
Head Rotation in Denoising Diffusion Models
Denoising Diffusion Models (DDM) are emerging as the cutting-edge technology in the realm of deep generative modeling, challenging the dominance of Generative Adversarial Networks.
Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency
acit-lausanne/refacing-cgan
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To evaluate the performance of the proposed de-identification tool, a comparative study was conducted between several existing defacing and refacing tools, with two different segmentation algorithms (FAST and Morphobox).
Identity-Preserving Talking Face Generation with Landmark and Appearance Priors
Weizhi-Zhong/IP_LAP
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CVPR 2023
Prior landmark characteristics of the speaker's face are employed to make the generated landmarks coincide with the facial outline of the speaker.
Laughing Matters: Introducing Laughing-Face Generation using Diffusion Models
antonibigata/Laughing-Matters
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Speech-driven animation has gained significant traction in recent years, with current methods achieving near-photorealistic results.
High-Fidelity 3D Face Generation from Natural Language Descriptions
zhuhao-nju/describe3d
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CVPR 2023
Synthesizing high-quality 3D face models from natural language descriptions is very valuable for many applications, including avatar creation, virtual reality, and telepresence.
