{"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/the-garden-of-forking-paths-towards-multi","title":"The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction","arxiv_id":"1912.06445","date":"2019-12-13","proceeding":"CVPR 2020 6","authors":["Junwei Liang","Lu Jiang","Kevin Murphy","Ting Yu","Alexander Hauptmann"],"abstract":"This paper studies the problem of predicting the distribution over multiple possible future paths of people as they move through various visual scenes. We make two main contributions. The first contribution is a new dataset, created in a realistic 3D simulator, which is based on real world trajectory data, and then extrapolated by human annotators to achieve different latent goals. This provides the first benchmark for quantitative evaluation of the models to predict multi-future trajectories. The second contribution is a new model to generate multiple plausible future trajectories, which contains novel designs of using multi-scale location encodings and convolutional RNNs over graphs. We refer to our model as Multiverse. We show that our model achieves the best results on our dataset, as well as on the real-world VIRAT/ActEV dataset (which just contains one possible future).","url_abs":"https://arxiv.org/abs/1912.06445v3","url_pdf":"https://arxiv.org/pdf/1912.06445v3.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":"the-garden-of-forking-paths-towards-multi","repo_url":"https://github.com/JunweiLiang/Multiverse","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"human-motion-prediction","task_name":"Human motion prediction"},{"task_slug":"multi-future-trajectory-prediction-1","task_name":"Multi Future Trajectory Prediction"},{"task_slug":"multi-future-trajectory-prediction","task_name":"Multi-future Trajectory Prediction"},{"task_slug":"self-driving-cars","task_name":"Self-Driving Cars"},{"task_slug":"trajectory-forecasting","task_name":"Trajectory Forecasting"},{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"},{"task_slug":"motion-prediction","task_name":"motion prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/trajectory-forecasting-on-forkingpaths","task":"Trajectory Forecasting","dataset":"ForkingPaths","model":"Multiverse","rank_in_archive_order":1,"of":1,"metrics":{"ADE":"168.9"},"uses_additional_data":false},{"leaderboard":"/sota/trajectory-prediction-on-actev","task":"Trajectory Prediction","dataset":"ActEV","model":"Multiverse","rank_in_archive_order":4,"of":4,"metrics":{"ADE-8/12":"18.51","FDE-8/12":"35.84"},"uses_additional_data":false},{"leaderboard":"/sota/trajectory-prediction-on-forkingpaths","task":"Trajectory Prediction","dataset":"ForkingPaths","model":"Multiverse","rank_in_archive_order":1,"of":1,"metrics":{"ADE":"168.9"},"uses_additional_data":false},{"leaderboard":"/sota/trajectory-prediction-on-stanford-drone","task":"Trajectory Prediction","dataset":"Stanford Drone","model":"Multiverse","rank_in_archive_order":14,"of":24,"metrics":{"ADE-8/12 @K = 20":"14.78","FDE-8/12 @K= 20":"27.09"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1912.06445","atlas_url":"https://app.syntology.ai/?focus=1912.06445","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.06445"}},"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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