most citedGenerative Video Propagation

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV2025

DreamOmni2: Multimodal Instruction-based Editing and Generation

Bin Xia, Bohao Peng, Yuechen Zhang +10

Recent advancements in instruction-based image editing and subject-driven generation have garnered significant attention, yet both tasks still face limitations in meeting practical…

cs.CV2025

DreamVE: Unified Instruction-based Image and Video Editing

Bin Xia, Jiyang Liu, Yuechen Zhang +6

Instruction-based editing holds vast potential due to its simple and efficient interactive editing format. However, instruction-based editing, particularly for video, has been cons…

cs.CV2025

VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning

Senqiao Yang, Junyi Li, Xin Lai +3

Recent advancements in vision-language models (VLMs) have improved performance by increasing the number of visual tokens, which are often significantly longer than text tokens. How…

cs.CV2025

Does Your Vision-Language Model Get Lost in the Long Video Sampling Dilemma?

Tianyuan Qu, Longxiang Tang, Bohao Peng +3

The rise of Large Vision-Language Models (LVLMs) has significantly advanced video understanding. However, efficiently processing long videos remains a challenge due to the ``Sampli…

cs.CV20241 cited

Generative Video Propagation

Shaoteng Liu, Tianyu Wang, Jui-Hsien Wang +8

Large-scale video generation models have the inherent ability to realistically model natural scenes. In this paper, we demonstrate that through a careful design of a generative vid…

cs.CV2024

DreamOmni: Unified Image Generation and Editing

Bin Xia, Yuechen Zhang, Jingyao Li +5

Currently, the success of large language models (LLMs) illustrates that a unified multitasking approach can significantly enhance model usability, streamline deployment, and foster…