Future Human Pose

Predicting future human poses from observed motion sequences is a crucial area of research in computer vision and robotics, aiming to improve human-robot interaction and enhance our understanding of human behavior. Current research focuses on developing robust and accurate prediction models using various deep learning architectures, including graph convolutional networks, transformers, and even surprisingly effective simpler models like multi-layer perceptrons, often incorporating multimodal data like LiDAR point clouds and gaze information to improve prediction accuracy and account for environmental context. This field is significant for advancing human-centered technologies, enabling safer and more efficient human-robot collaboration in diverse applications such as autonomous driving and assistive robotics.