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Face Reenactment

Face Reenactment is an emerging conditional face synthesis task that aims at fulfilling two goals simultaneously: 1) transfer a source face shape to a target face; while 2) preserve the appearance and the identity of the target face.

Source: One-shot Face Reenactment

Papers

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Showing 26–50 of 68 papers

TitleStatusHype
One-shot Face ReenactmentCode0
ReenactGAN: Learning to Reenact Faces via Boundary TransferCode0
MagicPortrait: Temporally Consistent Face Reenactment with 3D Geometric GuidanceCode0
One-Shot Face Reenactment on Megapixels—0
One-shot Face Reenactment Using Appearance Adaptive Normalization—0
One-shot Neural Face Reenactment via Finding Directions in GAN's Latent Space—0
On the Vulnerability of DeepFake Detectors to Attacks Generated by Denoising Diffusion Models—0
Pareidolia Face Reenactment—0
Realistic Face Reenactment via Self-Supervised Disentangling of Identity and Pose—0
Single Source One Shot Reenactment using Weighted motion From Paired Feature Points—0
Thinking the Fusion Strategy of Multi-reference Face Reenactment—0
ToonTalker: Cross-Domain Face Reenactment—0
Towards a Simultaneous and Granular Identity-Expression Control in Personalized Face Generation—0
UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing—0
Reenact Anything: Semantic Video Motion Transfer Using Motion-Textual Inversion—0
Unsupervised Facial Performance Editing via Vector-Quantized StyleGAN Representations—0
ActGAN: Flexible and Efficient One-shot Face Reenactment—0
Anchored Diffusion for Video Face Reenactment—0
Automatic Face Reenactment—0
Compressing Video Calls using Synthetic Talking Heads—0
Detection of GAN-synthesized street videos—0
DiffusionAct: Controllable Diffusion Autoencoder for One-shot Face Reenactment—0
Dual-Generator Face Reenactment—0
EFHQ: Multi-purpose ExtremePose-Face-HQ dataset—0
Egocentric Videoconferencing—0
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