{"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/flow-grounded-spatial-temporal-video","title":"Flow-Grounded Spatial-Temporal Video Prediction from Still Images","arxiv_id":"1807.09755","date":"2018-07-25","proceeding":"ECCV 2018 9","authors":["Yijun Li","Chen Fang","Jimei Yang","Zhaowen Wang","Xin Lu","Ming-Hsuan Yang"],"abstract":"Existing video prediction methods mainly rely on observing multiple\nhistorical frames or focus on predicting the next one-frame. In this work, we\nstudy the problem of generating consecutive multiple future frames by observing\none single still image only. We formulate the multi-frame prediction task as a\nmultiple time step flow (multi-flow) prediction phase followed by a\nflow-to-frame synthesis phase. The multi-flow prediction is modeled in a\nvariational probabilistic manner with spatial-temporal relationships learned\nthrough 3D convolutions. The flow-to-frame synthesis is modeled as a generative\nprocess in order to keep the predicted results lying closer to the manifold\nshape of real video sequence. Such a two-phase design prevents the model from\ndirectly looking at the high-dimensional pixel space of the frame sequence and\nis demonstrated to be more effective in predicting better and diverse results.\nExtensive experimental results on videos with different types of motion show\nthat the proposed algorithm performs favorably against existing methods in\nterms of quality, diversity and human perceptual evaluation.","url_abs":"http://arxiv.org/abs/1807.09755v2","url_pdf":"http://arxiv.org/pdf/1807.09755v2.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":"flow-grounded-spatial-temporal-video","repo_url":"https://github.com/Yijunmaverick/FlowGrounded-VideoPrediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"video-prediction","task_name":"Video Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.09755","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}