3D Joint

3D joint modeling focuses on accurately representing and manipulating the interconnectedness of joints in various systems, from human bodies to robotic structures and even virtual representations of wireless networks. Current research emphasizes developing robust and efficient methods for 3D joint estimation and prediction, often employing deep learning architectures like transformers and generative adversarial networks, alongside optimization techniques to refine estimations and handle uncertainties. These advancements are crucial for applications ranging from autonomous driving and human-computer interaction to improved digital twin technologies for complex systems and more realistic animation. The ultimate goal is to achieve accurate, reliable, and computationally efficient 3D joint representations for diverse applications.