15 citations · 30 across the 9 of their papers we have counts for
9 papers · 1 filter
Follow-Your-Creation: Empowering 4D Creation through Video Inpainting
Yue Ma, Kunyu Feng, Xinhua Zhang +7
We introduce Follow-Your-Creation, a novel 4D video creation framework capable of both generating and editing 4D content from a single monocular video input. By leveraging a powerf…
ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning
David Junhao Zhang, Roni Paiss, Shiran Zada +7
Recently, breakthroughs in video modeling have allowed for controllable camera trajectories in generated videos. However, these methods cannot be directly applied to user-provided…
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…
Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions
David Junhao Zhang, Dongxu Li, Hung Le +3
Most existing video diffusion models (VDMs) are limited to mere text conditions. Thereby, they are usually lacking in control over visual appearance and geometry structure of the g…
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…
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…
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…