Place Anything into Any Video
Place Anything into Any Video
Ziling Liu, Jinyu Yang, Mingqi Gao, Feng Zheng
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Demo Track. Pages 8729-8732.
https://doi.org/10.24963/ijcai.2024/1019
Controllable video editing has demonstrated remarkable potential across diverse applications, particularly in scenarios where capturing or re-capturing real-world videos is either impractical or costly. This paper introduces a novel and efficient system named Place-Anything, which facilitates the insertion of any object into any video solely based on a picture or text description of the target object or element. The system comprises three modules: 3D generation, video reconstruction, and 3D target insertion. This integrated approach offers an efficient and effective solution for producing and editing high-quality videos by naturally inserting realistic objects. Through experiment, we demonstrate that our system can effortlessly place any object into any video using just a photograph of the object. Our demo video can be found at https://youtu.be/afXqgLLRnTE. Please also visit our project page https://place-anything.github.io to get more information.
Keywords:
Computer Vision: CV: Applications
Computer Vision: CV: Scene analysis and understanding
Computer Vision: CV: Video analysis and understanding