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Viewport prediction is a crucial aspect of tile-based 360 video streamingsystem. However, existing trajectory based methods lack of robustness, alsooversimplify the process of information construction and fusion betweendifferent modality inputs, leading to the error accumulation problem. In thispaper, we propose a tile classification based viewport prediction method withMulti-modal Fusion Transformer, namely MFTR. Specifically, MFTR utilizestransformer-based networks to extract the long-range dependencies within eachmodality, then mine intra- and inter-modality relations to capture the combinedimpact of user historical inputs and video contents on future viewportselection. In addition, MFTR categorizes future tiles into two categories: userinterested or not, and selects future viewport as the region that contains mostuser interested tiles. Comparing with predicting head trajectories, choosingfuture viewport based on tile's binary classification results exhibits betterrobustness and interpretability. To evaluate our proposed MFTR, we conductextensive experiments on two widely used PVS-HM and Xu-Gaze dataset. MFTR showssuperior performance over state-of-the-art methods in terms of averageprediction accuracy and overlap ratio, also presents competitive computationefficiency.

Tile Classification Based Viewport Prediction with Multi-modal Fusion Transformer