CONSISTENT AND DIVERSE HUMAN MOTION PREDICTION USING CONDITIONAL VARIATIONAL AUTOENCODER WITH CONTEXT-AWARE LATENT SPACE

3D human motion prediction is the task of forecasting future motions from past observations. Previous work predicts diverse motions to cover uncertain future possibilities over a longer time. However, since most of them do not consider the surrounding context and use only the target person’s motion as the past observation, they generate overly diverse predictions rather than actual possibilities. Therefore, we propose a model that narrows down the possibilities by considering the context, especially interactions with nearby objects. Our proposed model predicts motions based on latent codes diversely sampled from a context-aware latent space. The experimental results show that our model improves accuracy and generates diverse predictions by focusing on possibilities that are consistent with object locations.