Motion Model

Motion models aim to represent and predict the movement of objects or agents, crucial for various applications like autonomous driving, animation, and robotics. Current research emphasizes developing robust and generalizable models, often employing deep learning architectures such as transformers, recurrent neural networks, and diffusion models, alongside techniques like Kalman filtering and Gaussian processes to handle uncertainty and noise. These advancements are improving the accuracy and efficiency of motion prediction and generation across diverse domains, impacting fields ranging from computer vision and robotics to virtual reality and healthcare.