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20182023
most citedPose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation

24 citations · 136 across the 26 of their papers we have counts for

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Showing 2022Show all

15 papers · 1 filter

cs.CV2022

Masked Lip-Sync Prediction by Audio-Visual Contextual Exploitation in Transformers

Yasheng Sun, Hang Zhou, Kaisiyuan Wang +7

Previous studies have explored generating accurately lip-synced talking faces for arbitrary targets given audio conditions. However, most of them deform or generate the whole facia…

cs.CV2022★ 14 cited

Audio-Driven Co-Speech Gesture Video Generation

Xian Liu, Qianyi Wu, Hang Zhou +4

Co-speech gesture is crucial for human-machine interaction and digital entertainment. While previous works mostly map speech audio to human skeletons (e.g., 2D keypoints), directly…

cs.CV2022★ 17 cited

Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition

Jiaxiang Tang, Kaisiyuan Wang, Hang Zhou +6

While dynamic Neural Radiance Fields (NeRF) have shown success in high-fidelity 3D modeling of talking portraits, the slow training and inference speed severely obstruct their pote…

cs.CV2022★ 1 cited

StyleSwap: Style-Based Generator Empowers Robust Face Swapping

Zhiliang Xu, Hang Zhou, Zhibin Hong +7

Numerous attempts have been made to the task of person-agnostic face swapping given its wide applications. While existing methods mostly rely on tedious network and loss designs, t…

cs.CV2022★ 13 cited

StyleFaceV: Face Video Generation via Decomposing and Recomposing Pretrained StyleGAN3

Haonan Qiu, Yuming Jiang, Hang Zhou +2

Realistic generative face video synthesis has long been a pursuit in both computer vision and graphics community. However, existing face video generation methods tend to produce lo…

cs.CV2022★ 6 cited

Detecting Deepfake by Creating Spatio-Temporal Regularity Disruption

Jiazhi Guan, Hang Zhou, Mingming Gong +3

Despite encouraging progress in deepfake detection, generalization to unseen forgery types remains a significant challenge due to the limited forgery clues explored during training…