Search Results for author: Jay Zhangjie Wu

Found 11 papers, 8 papers with code

VideoSwap: Customized Video Subject Swapping with Interactive Semantic Point Correspondence

no code implementations CVPR 2024 YuChao Gu, Yipin Zhou, Bichen Wu, Licheng Yu, Jia-Wei Liu, Rui Zhao, Jay Zhangjie Wu, David Junhao Zhang, Mike Zheng Shou, Kevin Tang

In contrast to previous methods that rely on dense correspondences, we introduce the VideoSwap framework that exploits semantic point correspondences, inspired by our observation that only a small number of semantic points are necessary to align the subject's motion trajectory and modify its shape.

Video Editing

DynVideo-E: Harnessing Dynamic NeRF for Large-Scale Motion- and View-Change Human-Centric Video Editing

no code implementations CVPR 2024 Jia-Wei Liu, Yan-Pei Cao, Jay Zhangjie Wu, Weijia Mao, YuChao Gu, Rui Zhao, Jussi Keppo, Ying Shan, Mike Zheng Shou

To overcome this, we propose to introduce the dynamic Neural Radiance Fields (NeRF) as the innovative video representation, where the editing can be performed in the 3D spaces and propagated to the entire video via the deformation field.

Style Transfer Super-Resolution +1

MotionDirector: Motion Customization of Text-to-Video Diffusion Models

1 code implementation12 Oct 2023 Rui Zhao, YuChao Gu, Jay Zhangjie Wu, David Junhao Zhang, Jiawei Liu, Weijia Wu, Jussi Keppo, Mike Zheng Shou

Given a set of video clips of the same motion concept, the task of Motion Customization is to adapt existing text-to-video diffusion models to generate videos with this motion.

Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

1 code implementation27 Sep 2023 David Junhao Zhang, Jay Zhangjie Wu, Jia-Wei Liu, Rui Zhao, Lingmin Ran, YuChao Gu, Difei Gao, Mike Zheng Shou

In this paper, we are the first to propose a hybrid model, dubbed as Show-1, which marries pixel-based and latent-based VDMs for text-to-video generation.

Text-to-Video Generation Video Alignment +1

Label-Efficient Online Continual Object Detection in Streaming Video

1 code implementation ICCV 2023 Jay Zhangjie Wu, David Junhao Zhang, Wynne Hsu, Mengmi Zhang, Mike Zheng Shou

Remarkably, with only 25% annotated video frames, our method still outperforms the base CL learners, which are trained with 100% annotations on all video frames.

Continual Learning Hippocampus +3

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