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- W3137287462 abstract "Video frame interpolation is the task of creating an interface between twoadjacent frames along the time axis. So, instead of simply averaging twoadjacent frames to create an intermediate image, this operation should maintainsemantic continuity with the adjacent frames. Most conventional methods useoptical flow, and various tools such as occlusion handling and object smoothingare indispensable. Since the use of these various tools leads to complexproblems, we tried to tackle the video interframe generation problem withoutusing problematic optical flow. To enable this, we have tried to use a deepneural network with an invertible structure and developed an invertible U-Netwhich is a modified normalizing flow. In addition, we propose a learning methodwith a new consistency loss in the latent space to maintain semantic temporalconsistency between frames. The resolution of the generated image is guaranteedto be identical to that of the original images by using an invertible network.Furthermore, as it is not a random image like the ones by generative models,our network guarantees stable outputs without flicker. Through experiments, weconfirmed the feasibility of the proposed algorithm and would like to suggestinvertible U-Net as a new possibility for baseline in video frameinterpolation. This paper is meaningful in that it is the worlds first attemptto use invertible networks instead of optical flows for video interpolation." @default.
- W3137287462 created "2021-03-29" @default.
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- W3137287462 date "2021-03-17" @default.
- W3137287462 modified "2023-09-24" @default.
- W3137287462 title "The Invertible U-Net for Optical-Flow-free Video Interframe Generation." @default.
- W3137287462 hasPublicationYear "2021" @default.
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