Selective Social-Interaction via Individual Importance for Fast Human Trajectory Prediction
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Abstract (arXiv)
This paper presents an architecture for selecting important neighboring people to predict the primary person's trajectory. To achieve effective neighboring people selection, we propose a people selection module called the Importance Estimator which outputs the importance of each neighboring person for predicting the primary person's future trajectory. To prevent gradients from being blocked by non-differentiable operations when sampling surrounding people based on their importance, we employ the Gumbel Softmax for training. Experiments conducted on the JRDB dataset show that our method speeds up the process with competitive prediction accuracy.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 18 references that are in the map. First posted on arXiv 23 Jun 2025.
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References · 12
- 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
- 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 690
- 2020Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Predictioncited by 325
- 2019Human Motion Trajectory Prediction: A Surveycited by 232
- 2022Stochastic Trajectory Prediction via Motion Indeterminacy Diffusioncited by 185
- 2023EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoningcited by 97
- 2024SingularTrajectory: Universal Trajectory Predictor Using Diffusion Modelcited by 35
- 2023Social-Transmotion: Promptable Human Trajectory Predictioncited by 33
- 2023Fast Inference and Update of Probabilistic Density Estimation on Trajectory Predictioncited by 27
- 2023Robots That Can See: Leveraging Human Pose for Trajectory Predictioncited by 23
- 2021FloMo: Tractable Motion Prediction with Normalizing Flowscited by 21
- 2025Physical Plausibility-aware Trajectory Prediction via Locomotion Embodimentcited by 10
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