9 citations · 18 across the 13 of their papers we have counts for
14 papers
Echo-Infinity: Learning Evolving Memory for Real-Time Infinite Video Generation
Yuxuan Bian, Zeyue Xue, Songchun Zhang +9
We present Echo Infinity, an autoregressive (AR) framework towards real-time infinite video generation that employs a learnable evolving memory to dynamically filter, abstract, and…
Video-As-Prompt: Unified Semantic Control for Video Generation
Yuxuan Bian, Xin Chen, Zenan Li +4
Unified, generalizable semantic control in video generation remains a critical open challenge. Existing methods either introduce artifacts by enforcing inappropriate pixel-wise pri…
OmniMotion-X: Versatile Multimodal Whole-Body Motion Generation
Guowei Xu, Yuxuan Bian, Ailing Zeng +6
This paper introduces OmniMotion-X, a versatile multimodal framework for whole-body human motion generation, leveraging an autoregressive diffusion transformer in a unified sequenc…
EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning
Xuan Ju, Tianyu Wang, Yuqian Zhou +11
Recent advances in foundation models highlight a clear trend toward unification and scaling, showing emergent capabilities across diverse domains. While image generation and editin…
VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control
Yuxuan Bian, Zhaoyang Zhang, Xuan Ju +4
Video inpainting, which aims to restore corrupted video content, has experienced substantial progress. Despite these advances, existing methods, whether propagating unmasked region…
FullDiT: Multi-Task Video Generative Foundation Model with Full Attention
Xuan Ju, Weicai Ye, Quande Liu +6
Current video generative foundation models primarily focus on text-to-video tasks, providing limited control for fine-grained video content creation. Although adapter-based approac…
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…
Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code
Xuan Ju, Ailing Zeng, Yuxuan Bian +2
Text-guided diffusion models have revolutionized image generation and editing, offering exceptional realism and diversity. Specifically, in the context of diffusion-based editing,…