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Just this feature of the algorithm helps to make a better picture at the output. Context analysis The algorithm usually takes all the frames of the video provide to it and interpolates everything at once. As a rule this leads to situations when at the junction of two plans for example a hero in full face → a hero from the back the neural network gives out a mess from two scenes on which nothing is clear. With a single increase in the number of frames twice this is almost imperceptible. But with a multiple increase in frame rate the video is simply impossible to watch since it consists entirely of a hodgepodge.

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To level this effect a second neural network is include in the work which compares frames in search of strong differences in context. If the frames are significantly different they will go into different scenes which means that the output video will be watchable. Interpolation by a neural network At this stage the neural network is turne on which takes two C Level Executive List neighboring images compares them with a depth map and optical flow analysis we obtaine them in the previous stage . It then generates frames with an intermeiate image. Video assembly At the last stage it remains only to take all the frames provide by the neural network combine them with the frames that were originally in the video and select the correct frame rate. After that you can add sound to the video with peace of mind and enjoy the smooth picture.

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It is clearer in implementation. For example here is my favorite video interpolate with DAIN. Video interpolation technologies are developing very quickly so DAIN is considere obsolete by the standards of artificial intelligence. In a group of researchers from China revise the work of a number of similar algorithms the result was RIFE. What neural IT Cell Number network did I use For my good purpose and to save my eyesight I decide to use a newer interpolating transformer RIFE. The neural network itself uses an approach that does not apply the optical flow analysis method the first DAIN layer . The developers wrote their own algorithm which was traine on two giant high quality video datasets.

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