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TEMPORAL LONG-TERM FRAME INTERPOLATION USING U-NET DISCRIMINATOR

To improve the accuracy of frame interpolation over long time intervals in video data, we propose a frame interpolation model based on U-Net, which identifies each pixel. The two discriminators associated with existing interpolation methods are each extended to a U-Net configuration to discriminate videos and images for each pixel. By training the generator and discriminator based on the loss values by the UNet, accurate image generation can be achieved. We performed comparisons on the KTH video dataset, and compared the proposed method with the conventional method. The results indicated that the quantitative scores were almost the same, however the visual evaluation revealed that the proposed method produced more accurate results.