@inproceedings{8a880e74b6bf4a3988b35988dd4c7723,
title = "Intelligent Figure Replacement Platform Based on Deep Learning",
abstract = "We introduce an intelligent platform to automatically replace the figures in video. The platform consists of three parts: figure replacement, content synthesis and interactive output. The original figure can be replaced by target figure captured with green screen background. The synthesized video is harmonized to fit the original video style. Actor can interactive with the output video to achieve better artistic effects. For the input video, we used Siammask and the STTN algorithm to key out the targeted figure and synthesize the background. Then, we use green screen keying in UE4 to get new figures in green screen background. Finally, we use the RainNet algorithm to harmonize the style of the foreground and background. Different from the existing methods which require manual manipulation on the video, our platform enhances the video production efficiency while lowers the requirement of video creation skills.",
keywords = "Figure replacement, Green screen, Style transfer, UE4, Video restoration",
author = "Ying Ma and Di Zhang and Hongfei Wang and Haozhe Hon and Long Ye",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Singapore Pte Ltd.; 18th International Forum of Digital Multimedia Communication, IFTC 2021 ; Conference date: 03-12-2021 Through 04-12-2021",
year = "2022",
doi = "10.1007/978-981-19-2266-4_5",
language = "English",
isbn = "9789811922657",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "55--70",
editor = "Guangtao Zhai and Jun Zhou and Hua Yang and Ping An and Xiaokang Yang",
booktitle = "Digital TV and Wireless Multimedia Communications - 18th International Forum, IFTC 2021, Revised Selected Papers",
}