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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
MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets—0
MaskRenderer: 3D-Infused Multi-Mask Realistic Face Reenactment—0
Mesh Guided One-shot Face Reenactment using Graph Convolutional Networks—0
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
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
Face Adapter for Pre-Trained Diffusion Models with Fine-Grained ID and Attribute Control—0
FACEGAN: Facial Attribute Controllable rEenactment GAN—0
FaR-GAN for One-Shot Face Reenactment—0
Fast Facial Landmark Detection and Applications: A Survey—0
Fine-grained Identity Preserving Landmark Synthesis for Face Reenactment—0
FSGANv2: Improved Subject Agnostic Face Swapping and Reenactment—0
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