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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 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.
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 between consecutive frames. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) is the top international computer vision conferences. A VFI paper co-worked by Intelligent Vision Lab of Samsung R&D Institute China-Nanjing (SRC-Nanjing) and Nanjing University has been recently accepted by CVPR 2023.
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