@inproceedings{27791cea05bc4c9d94a54c9bd2c256d5,
title = "Cross-identity Video Motion Retargeting with Joint Transformation and Synthesis",
abstract = "In this paper, we propose a novel dual-branch Transformation-Synthesis network (TS-Net), for video motion retargeting. Given one subject video and one driving video, TS-Net can produce a new plausible video with the subject appearance of the subject video and motion pattern of the driving video. TS-Net consists of a warp-based transformation branch and a warp-free synthesis branch. The novel design of dual branches combines the strengths of deformation-grid-based transformation and warp-free generation for better identity preservation and robustness to occlusion in the synthesized videos. A mask-aware similarity module is further introduced to the transformation branch to reduce computational overhead. Experimental results on face and dance datasets show that TS-Net achieves better performance in video motion retargeting than several state-of-the-art models as well as its single-branch variants. Our code is available at https://github.com/nihaomiao/WACV23_TSNet.",
author = "Haomiao Ni and Yihao Liu and Huang, {Sharon X.} and Yuan Xue",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023 ; Conference date: 03-01-2023 Through 07-01-2023",
year = "2023",
doi = "10.1109/WACV56688.2023.00049",
language = "English (US)",
series = "Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "412--422",
booktitle = "Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023",
address = "United States",
}