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Video frame interpolation (VFI) aims to generate intermediate frames between consecutive frames. VFI is widely applied in industrial products, including slow-motion video generation, video editing, intelligent display devices, etc. Despite recent advances in deep learning bring performance improvement
Video frame interpolation (VFI) is a classic low-level vision task that synthesizes non-existent intermediate frames between original consecutive frames. Before and after frame interpolation, if the time interval of original frames is fixed, VFI can generate high-rate smoother videos; if the frame rate is fixed, VFI can produce slow-motion videos.
Video frame interpolation aims to increase the frame rate of videos, by synthesizing non-existent intermediate frames between original successive frames. With recent advances in optical flow, motion-based interpolation has developed into a promising framework. Estimating the bi-directional motions between input frames is a crucial step for most motion-based interpolation methods.
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Video Frame Interpolation (VFI) is a classic low-level vision task, which aims to increase the frame rate of videos by synthesizing non-existent intermediate frames. IEEE/CVF Conference on Computer Vision and Pattern Recognition is a top-tier conference in computer vision. Intelligent Vision Lab ofSamsung R&D Institute China-Nanjing (SRC-Nanjing) develops a novel unified pipeline for flow-guided VFI, and the research paper has been recently accepted by CVPR 2023.
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