Supplementary Material for Recurrent Video Restoration Transformer with Guided Deformable Attention

In this supplementary material, we first give more details on training and testing datasets, as well as evaluation metrics. Then, we provide more visual comparisons of different methods. For video super-resolution, we train the model on two different training datasets for scale factor 4. First, we generate low-resolution images by the MATLAB imresize function (i.e., bicubic degradation) and train the model on REDS [8]. REDS4 [17] (i.e., clip 000, 011, 015, 020) is used as the test set. Second, we train the model on Vimeo-90K [18] with two different degradations: bicubic and blur downsampling (Gaussian blur with σ = 1.6 followed by subsampling).