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This is challenging because human motion is\ninherently multimodal: given a history of human motion paths, there are many\nsocially plausible ways that people could move in the future. We tackle this\nproblem by combining tools from sequence prediction and generative adversarial\nnetworks: a recurrent sequence-to-sequence model observes motion histories and\npredicts future behavior, using a novel pooling mechanism to aggregate\ninformation across people. We predict socially plausible futures by training\nadversarially against a recurrent discriminator, and encourage diverse\npredictions with a novel variety loss. Through experiments on several datasets\nwe demonstrate that our approach outperforms prior work in terms of accuracy,\nvariety, collision avoidance, and computational complexity.","url_abs":"http://arxiv.org/abs/1803.10892v1","url_pdf":"http://arxiv.org/pdf/1803.10892v1.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":"social-gan-socially-acceptable-trajectories","repo_url":"https://github.com/agrimgupta92/sgan","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"social-gan-socially-acceptable-trajectories","repo_url":"https://github.com/amiryanj/socialways","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"social-gan-socially-acceptable-trajectories","repo_url":"https://github.com/cmubig/SPEC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"social-gan-socially-acceptable-trajectories","repo_url":"https://github.com/huang-xx/STGAT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"social-gan-socially-acceptable-trajectories","repo_url":"https://github.com/m-hasan-n/pooling","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"social-gan-socially-acceptable-trajectories","repo_url":"https://github.com/mirkozaff/aa-sgan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"social-gan-socially-acceptable-trajectories","repo_url":"https://github.com/rohanchandra30/TrackNPred","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"social-gan-socially-acceptable-trajectories","repo_url":"https://github.com/romi514/TLSGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"collision-avoidance","task_name":"Collision Avoidance"},{"task_slug":"motion-forecasting","task_name":"Motion Forecasting"},{"task_slug":"multi-future-trajectory-prediction","task_name":"Multi-future Trajectory Prediction"},{"task_slug":"navigate","task_name":"Navigate"},{"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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/trajectory-prediction-on-eth","task":"Trajectory Prediction","dataset":"ETH","model":"Social-GAN","rank_in_archive_order":4,"of":5,"metrics":{"Avg AMD/AMV 8/12":"1.42"},"uses_additional_data":false},{"leaderboard":"/sota/trajectory-prediction-on-stanford-drone","task":"Trajectory Prediction","dataset":"Stanford Drone","model":"Social GAN","rank_in_archive_order":18,"of":24,"metrics":{"ADE (8/12) @K=5":"27.25","ADE-8/12 @K = 20":"27.23","FDE(8/12) @K=5":"41.44","FDE-8/12 @K= 20":"41.44"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1803.10892","atlas_url":"https://app.syntology.ai/?focus=1803.10892","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.10892"}},"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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