Canonical Pose

Canonical pose research focuses on representing objects and humans in a standardized, consistent pose, simplifying tasks like 3D object pose estimation and human animation. Current research employs various neural network architectures, including implicit neural representations (like NeRFs) and equivariant networks, to learn canonical poses from diverse data sources, such as multi-view images, videos, and point clouds, often incorporating self-supervised learning techniques. This work is crucial for advancing applications in robotics (e.g., object manipulation), computer vision (e.g., 3D reconstruction), and virtual/augmented reality (e.g., realistic avatar creation), by enabling more robust and generalizable models.