Long Term Motion

Long-term motion prediction aims to accurately forecast the future movements of objects, particularly humans, over extended timeframes. Current research heavily utilizes deep learning architectures, such as transformers and recurrent neural networks, often incorporating techniques like adversarial training and state-space decomposition to improve prediction accuracy and robustness in complex, dynamic environments. This field is crucial for advancing robotics, autonomous navigation, and human-computer interaction, enabling safer and more efficient collaboration between humans and machines in shared spaces. Improved long-term motion prediction also enhances applications like video surveillance and 3D animation by enabling more realistic and nuanced representations of movement.