Pose Sequence

Pose sequence analysis focuses on understanding and modeling the temporal evolution of human or object poses, aiming to predict future poses, generate realistic animations, or analyze movement patterns. Current research heavily utilizes deep learning, employing transformer networks, graph convolutional networks, and diffusion models to capture spatial and temporal relationships within pose sequences, often incorporating techniques like attention mechanisms and recurrent structures. This field is crucial for applications ranging from animation and virtual reality to human-computer interaction, healthcare monitoring (e.g., gait analysis), and robotics, driving advancements in computer vision and related fields.