9 citations · 18 across the 13 of their papers we have counts for
7 papers · 1 filter
BrushEdit: All-In-One Image Inpainting and Editing
Yaowei Li, Yuxuan Bian, Xuan Ju +5
Image editing has advanced significantly with the development of diffusion models using both inversion-based and instruction-based methods. However, current inversion-based approac…
MotionCraft: Crafting Whole-Body Motion with Plug-and-Play Multimodal Controls
Yuxuan Bian, Ailing Zeng, Xuan Ju +4
Whole-body multimodal motion generation, controlled by text, speech, or music, has numerous applications including video generation and character animation. However, employing a un…
Image Inpainting Models are Effective Tools for Instruction-guided Image Editing
Xuan Ju, Junhao Zhuang, Zhaoyang Zhang +3
This is the technique report for the winning solution of the CVPR2024 GenAI Media Generation Challenge Workshop's Instruction-guided Image Editing track. Instruction-guided image e…
MiraData: A Large-Scale Video Dataset with Long Durations and Structured Captions
Xuan Ju, Yiming Gao, Zhaoyang Zhang +6
Sora's high-motion intensity and long consistent videos have significantly impacted the field of video generation, attracting unprecedented attention. However, existing publicly av…
Intervention-Aware Forecasting: Breaking Historical Limits from a System Perspective
Zhijian Xu, Hao Wang, Qiang Xu
Traditional time series forecasting methods predominantly rely on historical data patterns, neglecting external interventions that significantly shape future dynamics. Through cont…
BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion
Xuan Ju, Xian Liu, Xintao Wang +3
Image inpainting, the process of restoring corrupted images, has seen significant advancements with the advent of diffusion models (DMs). Despite these advancements, current DM ada…
Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning
Yuxuan Bian, Xuan Ju, Jiangtong Li +3
In this study, we present aLLM4TS, an innovative framework that adapts Large Language Models (LLMs) for time-series representation learning. Central to our approach is that we reco…