2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022
DOI: 10.1109/cvpr52688.2022.00201
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IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation

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Cited by 116 publications
(56 citation statements)
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“…This paradigm, referred to as flow-based VFI, was further developed in the learning-based VFI literature [24]. These methods adopted various techniques to enhance interpolation quality, including the use of contextual information [25], designing bespoke flow estimation module [1,7,8,24,[26][27][28][29], employing a coarse-to-fine refinement strategy [30][31][32][33], developing new warping operations [9,34,35] and adopting higher-order motion modelling with additional input frames [5,10,36]. Some researchers argue that the imposition of a oneto-one mapping between the target and source pixels can limit the ability of flow-based methods to handle complex motions.…”
Section: A Video Frame Interpolationmentioning
confidence: 99%
“…This paradigm, referred to as flow-based VFI, was further developed in the learning-based VFI literature [24]. These methods adopted various techniques to enhance interpolation quality, including the use of contextual information [25], designing bespoke flow estimation module [1,7,8,24,[26][27][28][29], employing a coarse-to-fine refinement strategy [30][31][32][33], developing new warping operations [9,34,35] and adopting higher-order motion modelling with additional input frames [5,10,36]. Some researchers argue that the imposition of a oneto-one mapping between the target and source pixels can limit the ability of flow-based methods to handle complex motions.…”
Section: A Video Frame Interpolationmentioning
confidence: 99%
“…Above objective can already supervise frame interpolation, however, it often drops into local minimum due to misaligned texture in large motion cases. To solve this problem, DAIN [6], SoftSplat [11] use additional pre-trained optical flow network, RIFE [17], IFRNet [32] leverage knowledge distillation from the teacher flow network. However, cascaded architecture and knowledge distillation can result in large inference delay and complicate training process.…”
Section: Loss Functionmentioning
confidence: 99%
“…[23] estimates the flow by sampling the 3D spatio-temporal neighborhood of every output pixel. [27], [28], [47] refine the estimation of intermediate flows in order to capture large and complex motions. [3], [24], [25], [26], [30] utilize bidirectional flows to warp frames and additional modules to address occlusion.…”
Section: Video Frame Interpolationmentioning
confidence: 99%