The strength of generative models trained on big datasets, producing excellent quality and precision, has enabled the area of image processing to make significant strides. However, video footage processing has yet to make significant advancements. Maintaining high temporal consistency might be difficult due to the neural networks’ innate unpredictability. The nature of video files presents another difficulty since they frequently contain lower-quality textures than their picture equivalents and demand more processing power. As a result, algorithms based on video drastically underperform those that are based on photos. This disparity raises the question of whether it is possible to effortlessly apply well-established image algorithms to video material while maintaining high temporal consistency.
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