13 citations · 27 across the 10 of their papers we have counts for
11 papers
OmniTransfer: All-in-one Framework for Spatio-temporal Video Transfer
Pengze Zhang, Yanze Wu, Mengtian Li +8
Videos convey richer information than images or text, capturing both spatial and temporal dynamics. However, most existing video customization methods rely on reference images or t…
InstructX: Towards Unified Visual Editing with MLLM Guidance
Chong Mou, Qichao Sun, Yanze Wu +5
With recent advances in Multimodal Large Language Models (MLLMs) showing strong visual understanding and reasoning, interest is growing in using them to improve the editing perform…
CDST: Color Disentangled Style Transfer for Universal Style Reference Customization
Shiwen Zhang, Zhuowei Chen, Lang Chen +1
We introduce Color Disentangled Style Transfer (CDST), a novel and efficient two-stream style transfer training paradigm which completely isolates color from style and forces the s…
MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset via Attention Routing
Zinan Guo, Pengze Zhang, Yanze Wu +3
Current multi-subject customization approaches encounter two critical challenges: the difficulty in acquiring diverse multi-subject training data, and attribute entanglement across…
DreamO: A Unified Framework for Image Customization
Chong Mou, Yanze Wu, Wenxu Wu +15
Recently, extensive research on image customization (e.g., identity, subject, style, background, etc.) demonstrates strong customization capabilities in large-scale generative mode…
PuLID: Pure and Lightning ID Customization via Contrastive Alignment
Zinan Guo, Yanze Wu, Zhuowei Chen +3
We propose Pure and Lightning ID customization (PuLID), a novel tuning-free ID customization method for text-to-image generation. By incorporating a Lightning T2I branch with a sta…
DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations
Tianhao Qi, Shancheng Fang, Yanze Wu +5
The diffusion-based text-to-image model harbors immense potential in transferring reference style. However, current encoder-based approaches significantly impair the text controlla…
AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos
Yanze Wu, Xintao Wang, Gen Li +1
This paper studies the problem of real-world video super-resolution (VSR) for animation videos, and reveals three key improvements for practical animation VSR. First, recent real-w…
MM-RealSR: Metric Learning based Interactive Modulation for Real-World Super-Resolution
Chong Mou, Yanze Wu, Xintao Wang +3
Interactive image restoration aims to restore images by adjusting several controlling coefficients, which determine the restoration strength. Existing methods are restricted in lea…
Towards Vivid and Diverse Image Colorization with Generative Color Prior
Yanze Wu, Xintao Wang, Yu Li +3
Colorization has attracted increasing interest in recent years. Classic reference-based methods usually rely on external color images for plausible results. A large image database…
Question Guided Modular Routing Networks for Visual Question Answering
Yanze Wu, Qiang Sun, Jianqi Ma +4
This paper studies the task of Visual Question Answering (VQA), which is topical in Multimedia community recently. Particularly, we explore two critical research problems existed i…