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Summary of Vlogger: Make Your Dream a Vlog, by Shaobin Zhuang et al.


Vlogger: Make Your Dream A Vlog

by Shaobin Zhuang, Kunchang Li, Xinyuan Chen, Yaohui Wang, Ziwei Liu, Yu Qiao, Yali Wang

First submitted to arxiv on: 17 Jan 2024

Categories

  • Main: Computer Vision and Pattern Recognition (cs.CV)
  • Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The proposed Vlogger system generates a minute-level video blog (vlog) from user descriptions, breaking through the bottleneck of existing video generation approaches. It leverages Large Language Model as Director and decomposes the task into four stages: Script, Actor, ShowMaker, and Voicer. The Vlogger design mimics human beings, combining top-down planning and bottom-up shooting to generate vlogs. A novel video diffusion model, ShowMaker, is introduced for generating video snippets, incorporating textual and visual prompts from Script and Actor. This approach achieves state-of-the-art performance on zero-shot T2V generation and prediction tasks.
Low GrooveSquid.com (original content) Low Difficulty Summary
The Vlogger system generates videos from user descriptions. It’s like a director making a movie! The system breaks down the task into four steps: scriptwriting, acting, filming, and voiceover. The filmmakers work together to create a video blog (vlog) that tells a story with many scenes. This is hard for computers to do because it requires understanding what’s happening in each scene. But Vlogger can do it! It uses special computer models to help the filmmakers work together. This makes the video more coherent and fun to watch. The system works really well, even making videos that are over 5 minutes long without losing its focus.

Keywords

* Artificial intelligence  * Diffusion model  * Large language model  * Zero shot