“Learning to Understand Human Behaviors through Visual Cues”

Understanding human behaviors through visual cues is a fundamental challenge in computer vision, with significant implications across various domains, including healthcare, AR/VR, and human-computer interaction. Accurately estimating poses and comprehending motion patterns allow us to infer intentions, predict actions, and enhance human-centric applications. In this talk, we will explore recent advancements in human perception, showcasing the potential of learning-based models in unlocking and interpreting the complexities of human behaviors. By leveraging these models, we can develop more intuitive and responsive systems that better understand and interact with humans.