Summary of Interactivevideo: User-centric Controllable Video Generation with Synergistic Multimodal Instructions, by Yiyuan Zhang et al.
InteractiveVideo: User-Centric Controllable Video Generation with Synergistic Multimodal Instructions
by Yiyuan Zhang, Yuhao Kang, Zhixin Zhang, Xiaohan Ding, Sanyuan Zhao, Xiangyu Yue
First submitted to arxiv on: 5 Feb 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 This paper introduces InteractiveVideo, a user-centric framework for video generation. Unlike traditional generative approaches that rely on user-provided images or text, InteractiveVideo allows users to interactively guide the generation process through various mechanisms, such as text and image prompts, painting, drag-and-drop, etc. The proposed Synergistic Multimodal Instruction mechanism integrates user inputs into the generative model, enabling cooperative and responsive interactions between users and the generation process. This approach enables iterative refinement of the generation result through precise user instructions. With InteractiveVideo, users can meticulously tailor key aspects of a video, including reference images, semantics, and motions, until their requirements are met. The framework is designed to facilitate dynamic interaction and fine-grained control over the generation process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for people to create videos that fit exactly what they want. They can use different tools like typing, drawing, or moving objects around on a screen to tell the computer how to make the video. The computer will then use this information to create a video that matches what you want. This is different from other methods where computers just try to guess what you want and might not get it right. |
Keywords
* Artificial intelligence * Generative model * Semantics
