SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–68 of 68 papers

TitleStatusHype
LI-Net: Large-Pose Identity-Preserving Face Reenactment Network—0
Single Source One Shot Reenactment using Weighted motion From Paired Feature Points—0
KoDF: A Large-scale Korean DeepFake Detection Dataset—0
One-shot Face Reenactment Using Appearance Adaptive Normalization—0
Fast Facial Landmark Detection and Applications: A Survey—0
FACEGAN: Facial Attribute Controllable rEenactment GAN—0
Mesh Guided One-shot Face Reenactment using Graph Convolutional Networks—0
Learning Identity-Invariant Motion Representations for Cross-ID Face Reenactment—0
FaR-GAN for One-Shot Face Reenactment—0
ActGAN: Flexible and Efficient One-shot Face Reenactment—0
Realistic Face Reenactment via Self-Supervised Disentangling of Identity and Pose—0
MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets—0
FSGAN: Subject Agnostic Face Swapping and ReenactmentCode0
One-shot Face ReenactmentCode0
FReeNet: Multi-Identity Face ReenactmentCode0
ICface: Interpretable and Controllable Face Reenactment Using GANsCode0
ReenactGAN: Learning to Reenact Faces via Boundary TransferCode0
Automatic Face Reenactment—0
Show:102550
← PrevPage 3 of 3Next →

No leaderboard results yet.