3D Human Registration

3D human registration aims to accurately align 3D models or scans of humans, crucial for applications like animation, medical imaging, and human-robot interaction. Current research focuses on developing robust and efficient algorithms, including iterative closest point (ICP) variations and neural network-based approaches like neural ICP (NICP) and those leveraging diffusion models and deep functional maps, to handle challenges such as pose variations, noise, and non-rigid deformations. These advancements improve the accuracy and speed of registration, enabling more sophisticated applications and facilitating the analysis of large-scale human datasets. The resulting improvements in accuracy and efficiency are driving progress in fields ranging from medical diagnostics to virtual reality.