activity
20202025
most citedDFGC 2021: A DeepFake Game Competition

19 citations · 27 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CV2025

AudCast: Audio-Driven Human Video Generation by Cascaded Diffusion Transformers

Jiazhi Guan, Kaisiyuan Wang, Zhiliang Xu +12

Despite the recent progress of audio-driven video generation, existing methods mostly focus on driving facial movements, leading to non-coherent head and body dynamics. Moving forw…

cs.CV2025

Cosh-DiT: Co-Speech Gesture Video Synthesis via Hybrid Audio-Visual Diffusion Transformers

Yasheng Sun, Zhiliang Xu, Hang Zhou +9

Co-speech gesture video synthesis is a challenging task that requires both probabilistic modeling of human gestures and the synthesis of realistic images that align with the rhythm…

cs.CV2024

TALK-Act: Enhance Textural-Awareness for 2D Speaking Avatar Reenactment with Diffusion Model

Jiazhi Guan, Quanwei Yang, Kaisiyuan Wang +9

Recently, 2D speaking avatars have increasingly participated in everyday scenarios due to the fast development of facial animation techniques. However, most existing works neglect…

cs.CV2024

ReSyncer: Rewiring Style-based Generator for Unified Audio-Visually Synced Facial Performer

Jiazhi Guan, Zhiliang Xu, Hang Zhou +10

Lip-syncing videos with given audio is the foundation for various applications including the creation of virtual presenters or performers. While recent studies explore high-fidelit…

cs.CV2023★ 2 cited

Learning in a Single Domain for Non-Stationary Multi-Texture Synthesis

Xudong Xie, Zhen Zhu, Zijie Wu +2

This paper aims for a new generation task: non-stationary multi-texture synthesis, which unifies synthesizing multiple non-stationary textures in a single model. Most non-stationar…

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…