Cross-Attention Transformer for Video Interpolation
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We propose TAIN (Transformers and Attention for video INterpolation), a residual neural network for video interpolation, which aims to interpolate an intermediate frame given two consecutive image frames around it. We first present a novel visual transformer module, named Cross-Similarity (CS), to globally aggregate input image features with similar appearance as those of the predicted interpolated frame. These CS features are then used to refine the interpolated prediction. To account for occlusions in the CS features, we propose an Image Attention (IA) module to allow the network to focus on CS features from one frame over those of the other. Additionally, we augment our training dataset with an occluder patch that moves across frames to improve the network's robustness to occlusions and large motion. Because existing methods yield smooth predictions especially near MBs, we use an additional training loss based on image gradient to yield sharper predictions. TAIN outperforms existing methods that do not require flow estimation and performs comparably to flow-based methods while being computationally efficient in terms of inference time on Vimeo90k, UCF101, and SNU-FILM benchmarks.
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