Uncertainty-aware calibrated 3D human motion forecasting with latent conformal prediction

3D human motion forecasting aims to predict the future dynamics of observed human movements, with applications ranging from autonomous driving to robotics. Estimating the uncertainty of each individual prediction is crucial for risk-bounded planning and control to ensure safety. However, generative model-based approaches struggle with uncertainty quantification due to their implicit probabilistic representations. To address this, we propose an uncertainty-aware probabilistic forecasting framework that parameterize complex human motions using invertible networks and forecast parameters of the future human motion distribution. This explicit probabilistic representation offers effective uncertainty quantification based on probability density. Additionally, to transform heuristic notions of uncertainty into statistically grounded estimates, we introduce a copula-based latent conformal prediction method for calibrating the predicted distribution. Experiments demonstrate the strong predictive performance of our approach in both deterministic and diverse setup, and validate the effectiveness of the uncertainty estimates.