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Volumetric video emerges as a new attractive video paradigm in recent yearssince it provides an immersive and interactive 3D viewing experience with sixdegree-of-freedom (DoF). Unlike traditional 2D or panoramic videos, volumetricvideos require dense point clouds, voxels, meshes, or huge neural models todepict volumetric scenes, which results in a prohibitively high bandwidthburden for video delivery. Users' behavior analysis, especially the viewportand gaze analysis, then plays a significant role in prioritizing the contentstreaming within users' viewport and degrading the remaining content tomaximize user QoE with limited bandwidth. Although understanding user behavioris crucial, to the best of our best knowledge, there are no available 3Dvolumetric video viewing datasets containing fine-grained user interactivityfeatures, not to mention further analysis and behavior prediction. In thispaper, we for the first time release a volumetric video viewing behaviordataset, with a large scale, multiple dimensions, and diverse conditions. Weconduct an in-depth analysis to understand user behaviors when viewingvolumetric videos. Interesting findings on user viewport, gaze, and motionpreference related to different videos and users are revealed. We finallydesign a transformer-based viewport prediction model that fuses the features ofboth gaze and motion, which is able to achieve high accuracy at variousconditions. Our prediction model is expected to further benefit volumetricvideo streaming optimization. Our dataset, along with the correspondingvisualization tools is accessible athttps://cuhksz-inml.github.io/user-behavior-in-vv-watching/

Understanding User Behavior in Volumetric Video Watching: Dataset, Analysis and Prediction