most citedEEGDM: Label-Efficient EEG Representation Learning with Generative Diffusion Model

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cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2024★ 1 cited

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…

cs.CV2024

big.LITTLE Vision Transformer for Efficient Visual Recognition

He Guo, Yulong Wang, Zixuan Ye +2

In this paper, we introduce the big.LITTLE Vision Transformer, an innovative architecture aimed at achieving efficient visual recognition. This dual-transformer system is composed…