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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 1–25 of 68 papers

TitleStatusHype
MagicPortrait: Temporally Consistent Face Reenactment with 3D Geometric GuidanceCode0
StableAnimator: High-Quality Identity-Preserving Human Image AnimationCode5
G3FA: Geometry-guided GAN for Face Animation—0
Reenact Anything: Semantic Video Motion Transfer Using Motion-Textual Inversion—0
Anchored Diffusion for Video Face Reenactment—0
Learning Online Scale Transformation for Talking Head Video Generation—0
LivePortrait: Efficient Portrait Animation with Stitching and Retargeting ControlCode11
DF40: Toward Next-Generation Deepfake DetectionCode3
Face Adapter for Pre-Trained Diffusion Models with Fine-Grained ID and Attribute Control—0
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-pose, and Facial Expression Features—0
Deepfake Generation and Detection: A Benchmark and SurveyCode4
AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait AnimationCode9
DiffusionAct: Controllable Diffusion Autoencoder for One-shot Face Reenactment—0
One-shot Neural Face Reenactment via Finding Directions in GAN's Latent Space—0
Towards a Simultaneous and Granular Identity-Expression Control in Personalized Face Generation—0
Pose Adapted Shape Learning for Large-Pose Face ReenactmentCode1
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance Head-pose and Facial Expression Features—0
EFHQ: Multi-purpose ExtremePose-Face-HQ dataset—0
Learning Dense Correspondence for NeRF-Based Face Reenactment—0
BakedAvatar: Baking Neural Fields for Real-Time Head Avatar SynthesisCode2
MaskRenderer: 3D-Infused Multi-Mask Realistic Face Reenactment—0
ToonTalker: Cross-Domain Face Reenactment—0
HyperReenact: One-Shot Reenactment via Jointly Learning to Refine and Retarget FacesCode1
On the Vulnerability of DeepFake Detectors to Attacks Generated by Denoising Diffusion Models—0
ReliableSwap: Boosting General Face Swapping Via Reliable SupervisionCode2
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