Multi Person

Multi-person analysis focuses on understanding and modeling the complex interactions and behaviors of multiple individuals within a scene, primarily through computer vision techniques. Current research heavily utilizes transformer-based architectures, along with convolutional neural networks and graph neural networks, to address challenges like pose estimation, motion prediction, and activity recognition in multi-person scenarios, often incorporating scene context and physical constraints for improved accuracy. These advancements have significant implications for various fields, including human-computer interaction, virtual reality, robotics, and healthcare, by enabling more realistic and nuanced modeling of human behavior in complex environments.