  Volumetric video emerges as a new attractive video paradigm in recent years
since it provides an immersive and interactive 3D viewing experience with six
degree-of-freedom (DoF). Unlike traditional 2D or panoramic videos, volumetric
videos require dense point clouds, voxels, meshes, or huge neural models to
depict volumetric scenes, which results in a prohibitively high bandwidth
burden for video delivery. Users' behavior analysis, especially the viewport
and gaze analysis, then plays a significant role in prioritizing the content
streaming within users' viewport and degrading the remaining content to
maximize user QoE with limited bandwidth. Although understanding user behavior
is crucial, to the best of our best knowledge, there are no available 3D
volumetric video viewing datasets containing fine-grained user interactivity
features, not to mention further analysis and behavior prediction. In this
paper, we for the first time release a volumetric video viewing behavior
dataset, with a large scale, multiple dimensions, and diverse conditions. We
conduct an in-depth analysis to understand user behaviors when viewing
volumetric videos. Interesting findings on user viewport, gaze, and motion
preference related to different videos and users are revealed. We finally
design a transformer-based viewport prediction model that fuses the features of
both gaze and motion, which is able to achieve high accuracy at various
conditions. Our prediction model is expected to further benefit volumetric
video streaming optimization. Our dataset, along with the corresponding
visualization tools is accessible at
https://cuhksz-inml.github.io/user-behavior-in-vv-watching/
