15 citations · 30 across the 19 of their papers we have counts for
22 papers
GeONet: Geometry-Encoded Operator Network for Predicting Flow in Heterogeneous Porous Media
Md Lal Mamud, Maruti K. Mudunuru, Yucheng Fu +13
Rapid prediction of pressure-driven flow in microfluidic porous media is important for experimental design, tracer-test interpretation, and digital-twin development for subsurface…
Physis-Lang: Self-Evolving Language as a Physical Representation for Video World Model
Liming Lu, Xianzheng Ma, Wenkun He +14
Video world models are expected to predict how the physical world evolves, yet they often produce visually plausible videos that violate basic physical principles. Existing approac…
SolarWM: Open Data and Scalable Training for Long-Horizon Video World Models
Junchao Huang, Guian Fang, Shengju Qian +15
We introduce SolarWM, a fully open foundation for building interactive video world models from data preparation through long-horizon inference. Training across heterogeneous data s…
MIND: Benchmarking Memory Consistency and Action Control in World Models
Yixuan Ye, Xuanyu Lu, Yuxin Jiang +7
World models aim to understand, remember, and predict dynamic visual environments, yet a unified benchmark for evaluating their fundamental abilities remains lacking. To address th…
Olaf-World: Orienting Latent Actions for Video World Modeling
Yuxin Jiang, Yuchao Gu, Ivor W. Tsang +1
Scaling action-controllable world models is limited by the scarcity of action labels. While latent action learning promises to extract control interfaces from unlabeled video, lear…
Personalized Vision via Visual In-Context Learning
Yuxin Jiang, Yuchao Gu, Yiren Song +2
Modern vision models, trained on large-scale annotated datasets, excel at predefined tasks but struggle with personalized vision -- tasks defined at test time by users with customi…
UniVid: Unifying Vision Tasks with Pre-trained Video Generation Models
Lan Chen, Yuchao Gu, Qi Mao
Large language models, trained on extensive corpora, successfully unify diverse linguistic tasks within a single generative framework. Inspired by this, recent works like Large Vis…
FramePrompt: In-context Controllable Animation with Zero Structural Changes
Guian Fang, Yuchao Gu, Mike Zheng Shou
Generating controllable character animation from a reference image and motion guidance remains a challenging task due to the inherent difficulty of injecting appearance and motion…
Tuning-Free Image Editing with Fidelity and Editability via Unified Latent Diffusion Model
Qi Mao, Lan Chen, Yuchao Gu +2
Balancing fidelity and editability is essential in text-based image editing (TIE), where failures commonly lead to over- or under-editing issues. Existing methods typically rely on…
Long-Context Autoregressive Video Modeling with Next-Frame Prediction
Yuchao Gu, Weijia Mao, Mike Zheng Shou
Long-context video modeling is essential for enabling generative models to function as world simulators, as they must maintain temporal coherence over extended time spans. However,…
Edit Transfer: Learning Image Editing via Vision In-Context Relations
Lan Chen, Qi Mao, Yuchao Gu +1
We introduce a new setting, Edit Transfer, where a model learns a transformation from just a single source-target example and applies it to a new query image. While text-based meth…
ROICtrl: Boosting Instance Control for Visual Generation
Yuchao Gu, Yipin Zhou, Yunfan Ye +5
Natural language often struggles to accurately associate positional and attribute information with multiple instances, which limits current text-based visual generation models to s…
EvolveDirector: Approaching Advanced Text-to-Image Generation with Large Vision-Language Models
Rui Zhao, Hangjie Yuan, Yujie Wei +8
Recent advancements in generation models have showcased remarkable capabilities in generating fantastic content. However, most of them are trained on proprietary high-quality data,…
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
Jinheng Xie, Weijia Mao, Zechen Bai +7
We present a unified transformer, i.e., Show-o, that unifies multimodal understanding and generation. Unlike fully autoregressive models, Show-o unifies autoregressive and (discret…
DragAnything: Motion Control for Anything using Entity Representation
Weijia Wu, Zhuang Li, Yuchao Gu +7
We introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation. Comparison to existing motion control m…
Towards A Better Metric for Text-to-Video Generation
Jay Zhangjie Wu, Guian Fang, Haoning Wu +11
Generative models have demonstrated remarkable capability in synthesizing high-quality text, images, and videos. For video generation, contemporary text-to-video models exhibit imp…
MAG-Edit: Localized Image Editing in Complex Scenarios via Mask-Based Attention-Adjusted Guidance
Qi Mao, Lan Chen, Yuchao Gu +2
Recent diffusion-based image editing approaches have exhibited impressive editing capabilities in images with simple compositions. However, localized editing in complex scenarios h…
VideoSwap: Customized Video Subject Swapping with Interactive Semantic Point Correspondence
Yuchao Gu, Yipin Zhou, Bichen Wu +7
Current diffusion-based video editing primarily focuses on structure-preserved editing by utilizing various dense correspondences to ensure temporal consistency and motion alignmen…
MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers
Haoyu Ma, Shahin Mahdizadehaghdam, Bichen Wu +5
Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control. State-of-the-art approaches predominantly…
MotionDirector: Motion Customization of Text-to-Video Diffusion Models
Rui Zhao, Yuchao Gu, Jay Zhangjie Wu +5
Large-scale pre-trained diffusion models have exhibited remarkable capabilities in diverse video generations. Given a set of video clips of the same motion concept, the task of Mot…
DynVideo-E: Harnessing Dynamic NeRF for Large-Scale Motion- and View-Change Human-Centric Video Editing
Jia-Wei Liu, Yan-Pei Cao, Jay Zhangjie Wu +6
Despite recent progress in diffusion-based video editing, existing methods are limited to short-length videos due to the contradiction between long-range consistency and frame-wise…
Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation
David Junhao Zhang, Jay Zhangjie Wu, Jia-Wei Liu +5
Significant advancements have been achieved in the realm of large-scale pre-trained text-to-video Diffusion Models (VDMs). However, previous methods either rely solely on pixel-bas…