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arXiv · 2603.08658

Context-free Self-Conditioned GAN for Trajectory Forecasting

Tiago Rodrigues de Almeida·Eduardo Gutierrez Maestro·Oscar Martinez Mozos
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Abstract

In this paper, we present a context-free unsupervised approach based on a self-conditioned GAN to learn different modes from 2D trajectories. Our intuition is that each mode indicates a different behavioral moving pattern in the discriminator's feature space. We apply this approach to the problem of trajectory forecasting. We present three different training settings based on self-conditioned GAN, which produce better forecasters. We test our method in two data sets: human motion and road agents. Experimental results show that our approach outperforms previous context-free methods in the least representative supervised labels while performing well in the remaining labels. In addition, our approach outperforms globally in human motion, while performing well in road agents.

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Tiago Rodrigues de Almeida, Eduardo Gutierrez Maestro, Oscar Martinez Mozos. 2026-03-09. Context-free Self-Conditioned GAN for Trajectory Forecasting. https://doi.org/10.1109/icmla55696.2022.00196

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