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.

翻译：本文提出一种基于自条件生成对抗网络的无监督无上下文方法，用于从二维轨迹中学习不同模态。我们的核心观点是：在判别器的特征空间中，每个模态对应着不同的行为运动模式。我们将此方法应用于轨迹预测问题，提出了三种基于自条件生成对抗网络的训练方案，这些方案能够产生更优的预测模型。我们在人体运动和道路智能体两类数据集上验证了方法的有效性。实验结果表明：在监督标签最稀疏的场景下，我们的方法优于现有无上下文方法；而在其他标签条件下仍保持优异性能。此外，我们的方法在人体运动数据集上全面超越基线模型，在道路智能体数据集上也表现出良好性能。