Toward Reliable Human Pose Forecasting with Uncertainty

Recently, there has been an arms race of pose forecasting methods aimed at solving the spatio-temporal task of predicting a sequence of future 3D poses of a person given a sequence of past observed ones. However, the lack of unified benchmarks and limited uncertainty analysis have hindered progress in the field. To address this, we first develop an open-source library for human pose forecasting, featuring multiple models, datasets, and standardized evaluation metrics, with the aim of promoting research and moving toward a unified and fair evaluation. Second, we devise two types of uncertainty in the problem to increase performance and convey better trust: 1) we propose a method for modeling Figure 1: We propose to model two kinds of uncertainty: aleatoric uncertainty by using uncertainty priors to inject 1) aleatoric uncertainty, learned by our model to capture knowledge about the behavior of uncertainty. This focuses the temporal evolution of uncertainty, which becomes more the capacity of the model in the direction of more meaningful prominent over time, as depicted by the lighter colors and supervision while reducing the number of learned parameters thicker bones for the right person; 2) epistemic uncertainty and improving stability; 2) we introduce a novel to detect out-of-distribution forecast poses coming from unseen approach for quantifying the epistemic uncertainty of any scenarios in training, such as for the left person.