Pose Forecasting

Pose forecasting, the prediction of future human body poses from past observations, aims to improve human-robot interaction and other applications requiring anticipation of human movement. Current research emphasizes accurate and efficient prediction of 3D poses, particularly in complex multi-agent scenarios and over longer time horizons, employing diverse model architectures such as transformers, graph convolutional networks, and neural ordinary differential equations. This field is significant for advancing robotics, virtual reality, and human-computer interaction by enabling more natural and safe collaboration between humans and machines.