The goal of Video Frame Interpolation is to synthesize several frames in the middle of two adjacent frames of the original video. Video Frame Interpolation can be applied to generate slow motion video, increase video frame rate, and frame recovery in video streaming.
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The proposed model then warps the input frames, depth maps, and contextual features based on the optical flow and local interpolation kernels for synthesizing the output frame.
Ranked #3 on Video Frame Interpolation on UCF101
Finally, the two input images are warped and linearly fused to form each intermediate frame.
Our method develops a deep fully convolutional neural network that takes two input frames and estimates pairs of 1D kernels for all pixels simultaneously.
Ranked #7 on Video Frame Interpolation on Middlebury
Rather than synthesizing missing LR video frames as VFI networks do, we firstly temporally interpolate LR frame features in missing LR video frames capturing local temporal contexts by the proposed feature temporal interpolation network.
Ranked #1 on Video Frame Interpolation on Vid4 - 4x upscaling
Many video enhancement algorithms rely on optical flow to register frames in a video sequence.
Ranked #6 on Video Frame Interpolation on Middlebury
In contrast, how to perform forward warping has seen less attention, partly due to additional challenges such as resolving the conflict of mapping multiple pixels to the same target location in a differentiable way.
Ranked #1 on Video Frame Interpolation on Vimeo90k
In addition to the cycle consistency loss, we propose two extensions: motion linearity loss and edge-guided training.
As Deep Neural Networks are becoming more popular, much of the attention is being devoted to Computer Vision problems that used to be solved with more traditional approaches.
Video frame interpolation is one of the most challenging tasks in video processing research.