{"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/slamp-stochastic-latent-appearance-and-motion","title":"SLAMP: Stochastic Latent Appearance and Motion Prediction","arxiv_id":"2108.02760","date":"2021-08-05","proceeding":"ICCV 2021 10","authors":["Adil Kaan Akan","Erkut Erdem","Aykut Erdem","Fatma Güney"],"abstract":"Motion is an important cue for video prediction and often utilized by separating video content into static and dynamic components. Most of the previous work utilizing motion is deterministic but there are stochastic methods that can model the inherent uncertainty of the future. Existing stochastic models either do not reason about motion explicitly or make limiting assumptions about the static part. In this paper, we reason about appearance and motion in the video stochastically by predicting the future based on the motion history. Explicit reasoning about motion without history already reaches the performance of current stochastic models. The motion history further improves the results by allowing to predict consistent dynamics several frames into the future. Our model performs comparably to the state-of-the-art models on the generic video prediction datasets, however, significantly outperforms them on two challenging real-world autonomous driving datasets with complex motion and dynamic background.","url_abs":"https://arxiv.org/abs/2108.02760v1","url_pdf":"https://arxiv.org/pdf/2108.02760v1.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":"slamp-stochastic-latent-appearance-and-motion","repo_url":"https://github.com/kaanakan/slamp","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"video-generation","task_name":"Video Generation"},{"task_slug":"video-prediction","task_name":"Video Prediction"},{"task_slug":"motion-prediction","task_name":"motion prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-generation-on-bair-robot-pushing","task":"Video Generation","dataset":"BAIR Robot Pushing","model":"SLAMP","rank_in_archive_order":22,"of":31,"metrics":{"Cond":"2","FVD score":"245 ± 5","LPIPS":"0.0596±0.0032","PSNR":"19.67±0.26","Pred":"28","SSIM":"0.8175±0.084","Train":"10"},"uses_additional_data":false},{"leaderboard":"/sota/video-prediction-on-cityscapes-128x128","task":"Video Prediction","dataset":"Cityscapes 128x128","model":"SLAMP","rank_in_archive_order":4,"of":5,"metrics":{"Cond.":"10","LPIPS":"0.2941±0.022","PSNR":"21.73±0.76","Pred":"20","SSIM":"0.649±0.025"},"uses_additional_data":false},{"leaderboard":"/sota/video-prediction-on-kth","task":"Video Prediction","dataset":"KTH","model":"SLAMP","rank_in_archive_order":7,"of":31,"metrics":{"Cond":"10","FVD":"228 ± 5","LPIPS":"0.0795±0.0034","PSNR":"29.39±0.30","Pred":"30","SSIM":"0.8646±0.0050","Train":"10"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2108.02760","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.02760"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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