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We admire these progresses but are confused about the necessity: is there a simple method that can perform comparably well? This paper proposes SimVP, a simple video prediction model that is completely built upon CNN and trained by MSE loss in an end-to-end fashion. Without introducing any additional tricks and complicated strategies, we can achieve state-of-the-art performance on five benchmark datasets. Through extended experiments, we demonstrate that SimVP has strong generalization and extensibility on real-world datasets. The significant reduction of training cost makes it easier to scale to complex scenarios. We believe SimVP can serve as a solid baseline to stimulate the further development of video prediction. The code is available at \\href{https://github.com/gaozhangyang/SimVP-Simpler-yet-Better-Video-Prediction}{Github}.","url_abs":"https://arxiv.org/abs/2206.05099v1","url_pdf":"https://arxiv.org/pdf/2206.05099v1.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":"simvp-simpler-yet-better-video-prediction-1","repo_url":"https://github.com/gaozhangyang/simvp-simpler-yet-better-video-prediction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"simvp-simpler-yet-better-video-prediction-1","repo_url":"https://github.com/chengtan9907/OpenSTL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"simvp-simpler-yet-better-video-prediction-1","repo_url":"https://github.com/chengtan9907/simvpv2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"video-prediction","task_name":"Video Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-prediction-on-human36m","task":"Video Prediction","dataset":"Human3.6M","model":"SimVP","rank_in_archive_order":4,"of":9,"metrics":{"MAE":"1510","MSE":"316","SSIM":"0.904"},"uses_additional_data":false},{"leaderboard":"/sota/video-prediction-on-moving-mnist","task":"Video Prediction","dataset":"Moving MNIST","model":"SimVP","rank_in_archive_order":18,"of":31,"metrics":{"MSE":"23.8","SSIM":"0.948"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2206.05099","atlas_url":"https://app.syntology.ai/?focus=2206.05099","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.05099"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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