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Face Reenactment
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Detection of GAN-synthesized street videos
UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing
Egocentric Videoconferencing
LI-Net: Large-Pose Identity-Preserving Face Reenactment Network
Everything's Talkin': Pareidolia Face Reenactment
Single Source One Shot Reenactment using Weighted motion From Paired Feature Points
KoDF: A Large-scale Korean DeepFake Detection Dataset
One-shot Face Reenactment Using Appearance Adaptive Normalization
Fast Facial Landmark Detection and Applications: A Survey
FACEGAN: Facial Attribute Controllable rEenactment GAN
APB2FaceV2: Real-Time Audio-Guided Multi-Face Reenactment
SMILE: Semantically-guided Multi-attribute Image and Layout Editing
Mesh Guided One-shot Face Reenactment using Graph Convolutional Networks
FaR-GAN for One-Shot Face Reenactment
APB2Face: Audio-guided face reenactment with auxiliary pose and blink signals
ActGAN: Flexible and Efficient One-shot Face Reenactment
Realistic Face Reenactment via Self-Supervised Disentangling of Identity and Pose
MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets
FSGAN: Subject Agnostic Face Swapping and Reenactment
One-shot Face Reenactment
FReeNet: Multi-Identity Face Reenactment
ICface: Interpretable and Controllable Face Reenactment Using GANs
ReenactGAN: Learning to Reenact Faces via Boundary Transfer
Automatic Face Reenactment
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