{"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/social-ways-learning-multi-modal","title":"Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs","arxiv_id":"1904.09507","date":"2019-04-20","proceeding":"CVPR 2019 6","authors":["Javad Amirian","Jean-Bernard Hayet","Julien Pettre"],"abstract":"This paper proposes a novel approach for predicting the motion of pedestrians\ninteracting with others. It uses a Generative Adversarial Network (GAN) to\nsample plausible predictions for any agent in the scene. As GANs are very\nsusceptible to mode collapsing and dropping, we show that the recently proposed\nInfo-GAN allows dramatic improvements in multi-modal pedestrian trajectory\nprediction to avoid these issues. We also left out L2-loss in training the\ngenerator, unlike some previous works, because it causes serious mode\ncollapsing though faster convergence.\n  We show through experiments on real and synthetic data that the proposed\nmethod leads to generate more diverse samples and to preserve the modes of the\npredictive distribution. In particular, to prove this claim, we have designed a\ntoy example dataset of trajectories that can be used to assess the performance\nof different methods in preserving the predictive distribution modes.","url_abs":"http://arxiv.org/abs/1904.09507v2","url_pdf":"http://arxiv.org/pdf/1904.09507v2.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-ways-learning-multi-modal","repo_url":"https://github.com/amiryanj/socialways","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"human-motion-prediction","task_name":"Human motion prediction"},{"task_slug":"multi-future-trajectory-prediction","task_name":"Multi-future Trajectory Prediction"},{"task_slug":"pedestrian-trajectory-prediction","task_name":"Pedestrian 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"}],"methods":[{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"feedforward-network","method_name":"Feedforward Network"},{"method_slug":"infogan","method_name":"InfoGAN"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/trajectory-prediction-on-eth-biwi-walking","task":"Trajectory Prediction","dataset":"ETH BIWI Walking Pedestrians dataset","model":"Social Ways","rank_in_archive_order":1,"of":1,"metrics":{"ADE-8/12":"0.39"},"uses_additional_data":false},{"leaderboard":"/sota/trajectory-prediction-on-hotel-biwi-walking","task":"Trajectory Prediction","dataset":"Hotel BIWI Walking Pedestrians dataset","model":"Social Ways","rank_in_archive_order":1,"of":1,"metrics":{"ADE-8/12":"0.39"},"uses_additional_data":false},{"leaderboard":"/sota/trajectory-prediction-on-stanford-drone","task":"Trajectory Prediction","dataset":"Stanford Drone","model":"Social-Ways","rank_in_archive_order":24,"of":24,"metrics":{"ADE (in world coordinates)":"0.62","FDE (in world coordinates)":"1.16"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1904.09507","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}