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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 1–50 of 68 papers

TitleStatusHype
LivePortrait: Efficient Portrait Animation with Stitching and Retargeting ControlCode11
AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait AnimationCode9
StableAnimator: High-Quality Identity-Preserving Human Image AnimationCode5
Thin-Plate Spline Motion Model for Image AnimationCode4
Deepfake Generation and Detection: A Benchmark and SurveyCode4
DF40: Toward Next-Generation Deepfake DetectionCode3
StyleAvatar: Real-time Photo-realistic Portrait Avatar from a Single VideoCode2
BakedAvatar: Baking Neural Fields for Real-Time Head Avatar SynthesisCode2
3DFaceShop: Explicitly Controllable 3D-Aware Portrait GenerationCode2
ReliableSwap: Boosting General Face Swapping Via Reliable SupervisionCode2
Pose Adapted Shape Learning for Large-Pose Face ReenactmentCode1
Everything's Talkin': Pareidolia Face ReenactmentCode1
Finding Directions in GAN's Latent Space for Neural Face ReenactmentCode1
Initiative Defense against Facial ManipulationCode1
AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head ReenactmentCode1
HyperReenact: One-Shot Reenactment via Jointly Learning to Refine and Retarget FacesCode1
SMILE: Semantically-guided Multi-attribute Image and Layout EditingCode1
StyleMask: Disentangling the Style Space of StyleGAN2 for Neural Face ReenactmentCode1
APB2Face: Audio-guided face reenactment with auxiliary pose and blink signalsCode1
APB2FaceV2: Real-Time Audio-Guided Multi-Face ReenactmentCode1
Audio-Visual Face ReenactmentCode1
AI-generated characters for supporting personalized learning and well-beingCode1
Learning Identity-Invariant Motion Representations for Cross-ID Face Reenactment—0
Learning Online Scale Transformation for Talking Head Video Generation—0
LI-Net: Large-Pose Identity-Preserving Face Reenactment Network—0
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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