7 papers
UniVideo: Unified Understanding, Generation, and Editing for Videos
Cong Wei, Quande Liu, Zixuan Ye +5
Unified multimodal models have shown promising results in multimodal content generation and editing but remain largely limited to the image domain. In this work, we present UniVide…
Visual-Aware CoT: Achieving High-Fidelity Visual Consistency in Unified Models
Zixuan Ye, Quande Liu, Cong Wei +5
Recently, the introduction of Chain-of-Thought (CoT) has largely improved the generation ability of unified models. However, it is observed that the current thinking process during…
EEGDM: EEG Representation Learning via Generative Diffusion Model
Jia Hong Puah, Sim Kuan Goh, Ziwei Zhang +6
While electroencephalogram (EEG) has been a crucial tool for monitoring the brain and diagnosing neurological disorders (e.g., epilepsy), learning meaningful representations from r…
FullDiT2: Efficient In-Context Conditioning for Video Diffusion Transformers
Xuanhua He, Quande Liu, Zixuan Ye +7
Fine-grained and efficient controllability on video diffusion transformers has raised increasing desires for the applicability. Recently, In-context Conditioning emerged as a power…
UNIC: Unified In-Context Video Editing
Zixuan Ye, Xuanhua He, Quande Liu +7
Recent advances in text-to-video generation have sparked interest in generative video editing tasks. Previous methods often rely on task-specific architectures (e.g., additional ad…
StyleMaster: Stylize Your Video with Artistic Generation and Translation
Zixuan Ye, Huijuan Huang, Xintao Wang +3
Style control has been popular in video generation models. Existing methods often generate videos far from the given style, cause content leakage, and struggle to transfer one vide…