Pose Lifting

Pose lifting aims to reconstruct accurate 3D object or human poses from 2D image data, addressing the inherent ambiguity in projecting 3D information onto a 2D plane. Current research focuses on developing robust and generalizable models, employing architectures like transformers, graph convolutional networks, and diffusion models, often incorporating techniques like attention mechanisms and confidence-aware processing to handle occlusions and noisy input. This field is crucial for advancing applications such as augmented reality, robotics, and human-computer interaction by enabling more natural and intuitive interactions with 3D environments based on 2D visual input.