This paper studies the problem of predicting the distribution over multiple
possible future paths of people as they move through various visual scenes. We
make two main contributions. The first contribution is a new dataset, created
in a realistic 3D simulator, which is based on real world trajectory data, and
then extrapolated by human annotators to achieve different latent goals. This
provides the first benchmark for quantitative evaluation of the models to
predict multi-future trajectories. The second contribution is a new model to
generate multiple plausible future trajectories, which contains novel designs
of using multi-scale location encodings and convolutional RNNs over graphs. We
refer to our model as Multiverse. We show that our model achieves the best
results on our dataset, as well as on the real-world VIRAT/ActEV dataset (which
just contains one possible future).

本文提出了一个名为 Multiverse 的新模型来生成多个合理的未来人物运动路径，并使用 3D 模拟器创建了一个名为 VIRAT/ActEV 的数据集用以对该模型进行测试。通过使用多尺度位置编码和图卷积神经网络，该模型达到了最佳效果。