  Human motion prediction is still an open problem, which is extremely
important for autonomous driving and safety applications. Although there are
great advances in this area, the widely studied topic of adversarial attacks
has not been applied to multi-regression models such as GCNs and MLP-based
architectures in human motion prediction. This work intends to reduce this gap
using extensive quantitative and qualitative experiments in state-of-the-art
architectures similar to the initial stages of adversarial attacks in image
classification. The results suggest that models are susceptible to attacks even
on low levels of perturbation. We also show experiments with 3D transformations
that affect the model performance, in particular, we show that most models are
sensitive to simple rotations and translations which do not alter joint
distances. We conclude that similar to earlier CNN models, motion forecasting
tasks are susceptible to small perturbations and simple 3D transformations.
