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Found 5 papers, 5 papers with code
SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model
In this paper, we propose SingularTrajectory, a diffusion-based universal trajectory prediction framework to reduce the performance gap across the five tasks.
Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction
Next, to guide the language model in understanding and reasoning high-level knowledge, such as scene context and social relationships between pedestrians, we introduce an auxiliary multi-task question and answering.
EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting
In this paper, we present EigenTrajectory ($\mathbb{ET}$), a trajectory prediction approach that uses a novel trajectory descriptor to form a compact space, known here as $\mathbb{ET}$ space, in place of Euclidean space, for representing pedestrian movements.
Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction
A key idea of GP-Graph is to model both individual-wise and group-wise relations as graph representations.
Non-Probability Sampling Network for Stochastic Human Trajectory Prediction
Capturing multimodal natures is essential for stochastic pedestrian trajectory prediction, to infer a finite set of future trajectories.
