1 citations · 1 across the 5 of their papers we have counts for
7 papers
Making Avatars Interact: Towards Text-Driven Human-Object Interaction for Controllable Talking Avatars
Youliang Zhang, Zhengguang Zhou, Zhentao Yu +11
Generating talking avatars is a fundamental task in video generation. Although existing methods can generate full-body talking avatars with simple human motion, extending this task…
InstanceV: Instance-Level Video Generation
Yuheng Chen, Teng Hu, Jiangning Zhang +3
Recent advances in text-to-video diffusion models have enabled the generation of high-quality videos conditioned on textual descriptions. However, most existing text-to-video model…
Harmony: Harmonizing Audio and Video Generation through Cross-Task Synergy
Teng Hu, Zhentao Yu, Guozhen Zhang +6
The synthesis of synchronized audio-visual content is a key challenge in generative AI, with open-source models facing challenges in robust audio-video alignment. Our analysis reve…
UniAVGen: Unified Audio and Video Generation with Asymmetric Cross-Modal Interactions
Guozhen Zhang, Zixiang Zhou, Teng Hu +6
Due to the lack of effective cross-modal modeling, existing open-source audio-video generation methods often exhibit compromised lip synchronization and insufficient semantic consi…
PolyVivid: Vivid Multi-Subject Video Generation with Cross-Modal Interaction and Enhancement
Teng Hu, Zhentao Yu, Zhengguang Zhou +4
Despite recent advances in video generation, existing models still lack fine-grained controllability, especially for multi-subject customization with consistent identity and intera…
OmniV2V: Versatile Video Generation and Editing via Dynamic Content Manipulation
Sen Liang, Zhentao Yu, Zhengguang Zhou +8
The emergence of Diffusion Transformers (DiT) has brought significant advancements to video generation, especially in text-to-video and image-to-video tasks. Although video generat…