{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/very-long-term-field-of-view-prediction-for","title":"Very Long Term Field of View Prediction for 360-degree Video Streaming","arxiv_id":"1902.01439","date":"2019-02-04","proceeding":null,"authors":["Chenge Li","Weixi Zhang","Yong liu","Yao Wang"],"abstract":"360-degree videos have gained increasing popularity in recent years with the\ndevelopments and advances in Virtual Reality (VR) and Augmented Reality (AR)\ntechnologies. In such applications, a user only watches a video scene within a\nfield of view (FoV) centered in a certain direction. Predicting the future FoV\nin a long time horizon (more than seconds ahead) can help save bandwidth\nresources in on-demand video streaming while minimizing video freezing in\nnetworks with significant bandwidth variations. In this work, we treat the FoV\nprediction as a sequence learning problem, and propose to predict the target\nuser's future FoV not only based on the user's own past FoV center trajectory\nbut also other users' future FoV locations. We propose multiple prediction\nmodels based on two different FoV representations: one using FoV center\ntrajectories and another using equirectangular heatmaps that represent the FoV\ncenter distributions. Extensive evaluations with two public datasets\ndemonstrate that the proposed models can significantly outperform benchmark\nmodels, and other users' FoVs are very helpful for improving long-term\npredictions.","url_abs":"http://arxiv.org/abs/1902.01439v1","url_pdf":"http://arxiv.org/pdf/1902.01439v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"very-long-term-field-of-view-prediction-for","repo_url":"https://github.com/ChengeLi/LongTerm360FoV","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1902.01439","atlas_url":"https://app.syntology.ai/?focus=1902.01439","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}