Frequency-Based Motion Representation for Video Generative Adversarial Networks

Videos contain motions of various speeds. For example, the motions of one's head and mouth differ in terms of speed - the head being relatively stable and the mouth moving rapidly as one speaks. Despite its diverse nature, previous video GANs generate video based on a single unified motion repr...

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Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 32(2023) vom: 13., Seite 3949-3963
1. Verfasser: Hyun, Sangeek (VerfasserIn)
Weitere Verfasser: Lew, Jaihyun, Chung, Jiwoo, Kim, Euiyeon, Heo, Jae-Pil
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2023
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article
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520 |a Videos contain motions of various speeds. For example, the motions of one's head and mouth differ in terms of speed - the head being relatively stable and the mouth moving rapidly as one speaks. Despite its diverse nature, previous video GANs generate video based on a single unified motion representation without considering the aspect of speed. In this paper, we propose a frequency-based motion representation for video GANs to realize the concept of speed in video generation process. In detail, we represent motions as continuous sinusoidal signals of various frequencies by introducing a coordinate-based motion generator. We show, in that case, frequency is highly related to the speed of motion. Based on this observation, we present frequency-aware weight modulation that enables manipulation of motions within a specific range of speed, which could not be achieved with the previous techniques. Extensive experiments validate that the proposed method outperforms state-of-the-art video GANs in terms of generation quality by its capability to model various speed of motions. Furthermore, we also show that our temporally continuous representation enables to further synthesize intermediate and future frames of generated videos 
650 4 |a Journal Article 
700 1 |a Lew, Jaihyun  |e verfasserin  |4 aut 
700 1 |a Chung, Jiwoo  |e verfasserin  |4 aut 
700 1 |a Kim, Euiyeon  |e verfasserin  |4 aut 
700 1 |a Heo, Jae-Pil  |e verfasserin  |4 aut 
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