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

David Junhao Zhang; Jay Zhangjie Wu; Jiawei Liu; Rui Zhao; Lingmin Ran; Yuchao Gu; Difei Gao; Mike Zheng Shou

doi:10.1007/s11263-024-02271-9

ScienceGate Book Chapters

JOURNAL ARTICLE

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

David Junhao Zhang Jay Zhangjie Wu Jiawei Liu Rui Zhao Lingmin Ran Yuchao Gu Difei Gao Mike Zheng Shou

Year: 2024 Journal: International Journal of Computer Vision Vol: 133 (4)Pages: 1879-1893 Publisher: Springer Science+Business Media

DOI: 10.1007/s11263-024-02271-9

Get Full-Text PDF Get Analytical Report

Keywords:

Pixel Computer science Artificial intelligence Diffusion Pattern recognition (psychology) Computer vision

Metrics

Cited By

25.98

FWCI (Field Weighted Citation Impact)

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Generative Adversarial Networks and Image Synthesis

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Video Analysis and Summarization

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Human Motion and Animation

Physical Sciences → Engineering → Control and Systems Engineering

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

Metrics

Citation History

Topics

Related Documents

Text-to-Video Generation Using Latent Diffusion Models: Structural and Temporal Analysis

AudioLDM: Text-to-Audio Generation with Latent Diffusion Models

Grid Diffusion Models for Text-to-Video Generation

AudioLDM: Text-to-Audio Generation with Latent Diffusion Models

AudioLDM: Text-to-Audio Generation with Latent Diffusion Models