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- W4328006310 abstract "Viewing real-time human synthesis results on smart-phones provides users a convenient interactive experience. How-ever, most human synthesis solutions relay on high-performance servers and lack interaction with users. In order to reduce traffic between smartphones and cloud, this paper introduces a novel adaptive frame selection network (AFSN) on mobile devices. Besides, we propose a reinforcement-learning based agent that selects the image frames according to the pose change, that reduces communications and the amount of calculations. Based upon AFSN, we build a multi-task human synthesis system, that can deal with background changing, pose transfer, virtual try-on, and other generate tasks in a unified framework. Experimental results on one real-world dataset demonstrate that our method can reduce the number of frames to be processed by more than 50%." @default.
- W4328006310 created "2023-03-22" @default.
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- W4328006310 date "2022-08-01" @default.
- W4328006310 modified "2023-09-27" @default.
- W4328006310 title "AFSN: Adaptive Frame Selection Network for Efficient Human Synthesis on Smartphones" @default.
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- W4328006310 doi "https://doi.org/10.1109/bigcom57025.2022.00036" @default.
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