@inproceedings{
gao2024starvp,
title={{STAR}-{VP}: Improving Long-term Viewport Prediction in 360{\textdegree} Videos via Space-aligned and Time-varying Fusion},
author={Baoqi Gao and Daoxu Sheng and Lei Zhang and Qi Qi and Bo He and Zirui Zhuang and Jingyu Wang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=Mc3G3ciRSG}
}
Accurate long-term viewport prediction in tile-based 360° video adaptive streaming helps pre-download tiles for a further future, thus establishing a longer buffer to cope with network fluctuations. Long-term viewport motion is mainly influenced by Historical viewpoint Trajectory (HT) and Video Content information (VC). However, HT and VC are difficult to align in space due to their different modalities, and their relative importance in viewport prediction varies across prediction time steps. In this paper, we propose STAR-VP, a model that fuses HT and VC in a Space-aligned and Time-vARying manner for Viewport Prediction. Specifically, we first propose a novel saliency representation $salxyz$ and a Spatial Attention Module to solve the spatial alignment of HT and VC. Then, we propose a two-stage fusion approach based on Transformer and gating mechanisms to capture their time-varying importance. Visualization of attention scores intuitively demonstrates STAR-VP's capability in space-aligned and time-varying fusion. Evaluation on three public datasets shows that STAR-VP achieves state-of-the-art accuracy for long-term (2-5s) viewport prediction without sacrificing short-term ($<$1s) prediction performance.
STAR-VP: Improving Long-term Viewport Prediction in 360° Videos via Space-aligned and Time-varying Fusion
Baoqi Gao Daoxu Sheng Lei Zhang Qi Qi Bo He Zirui Zhuang Jingyu Wang