37 citations · 38 across the 8 of their papers we have counts for
8 papers
Learning to Discover Forgery Cues for Face Forgery Detection
Jiahe Tian, Peng Chen, Cai Yu +4
Locating manipulation maps, i.e., pixel-level annotation of forgery cues, is crucial for providing interpretable detection results in face forgery detection. Related learning objec…
Explicit Correlation Learning for Generalizable Cross-Modal Deepfake Detection
Cai Yu, Shan Jia, Xiaomeng Fu +6
With the rising prevalence of deepfakes, there is a growing interest in developing generalizable detection methods for various types of deepfakes. While effective in their specific…
OSM-Net: One-to-Many One-shot Talking Head Generation with Spontaneous Head Motions
Jin Liu, Xi Wang, Xiaomeng Fu +4
One-shot talking head generation has no explicit head movement reference, thus it is difficult to generate talking heads with head motions. Some existing works only edit the mouth…
MFR-Net: Multi-faceted Responsive Listening Head Generation via Denoising Diffusion Model
Jin Liu, Xi Wang, Xiaomeng Fu +4
Face-to-face communication is a common scenario including roles of speakers and listeners. Most existing research methods focus on producing speaker videos, while the generation of…
A Unified Framework for Modality-Agnostic Deepfakes Detection
Cai Yu, Peng Chen, Jiahe Tian +7
As AI-generated content (AIGC) thrives, deepfakes have expanded from single-modality falsification to cross-modal fake content creation, where either audio or visual components can…
FONT: Flow-guided One-shot Talking Head Generation with Natural Head Motions
Jin Liu, Xi Wang, Xiaomeng Fu +4
One-shot talking head generation has received growing attention in recent years, with various creative and practical applications. An ideal natural and vivid generated talking head…
OPT: One-shot Pose-Controllable Talking Head Generation
Jin Liu, Xi Wang, Xiaomeng Fu +4
One-shot talking head generation produces lip-sync talking heads based on arbitrary audio and one source face. To guarantee the naturalness and realness, recent methods propose to…
LI-Net: Large-Pose Identity-Preserving Face Reenactment Network
Jin Liu, Peng Chen, Tao Liang +5
Face reenactment is a challenging task, as it is difficult to maintain accurate expression, pose and identity simultaneously. Most existing methods directly apply driving facial la…