AI RESEARCH
Context-free Self-Conditioned GAN for Trajectory Forecasting
arXiv CS.LG
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ArXi:2603.08658v1 Announce Type: new 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