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20212024
most citedLearning to Discover Forgery Cues for Face Forgery Detection

37 citations · 38 across the 8 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.CV2023

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…

cs.CV2023

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…

cs.MM2023★ 1 cited

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…

cs.CV2023

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…

cs.CV2023

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…