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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 51–68 of 68 papers

TitleStatusHype
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
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-pose, and Facial Expression Features—0
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance Head-pose and Facial Expression Features—0
G3FA: Geometry-guided GAN for Face Animation—0
High-fidelity Facial Avatar Reconstruction from Monocular Video with Generative Priors—0
KoDF: A Large-scale Korean DeepFake Detection Dataset—0
Learning Dense Correspondence for NeRF-Based Face Reenactment—0
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
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