Category Agnostic Pose

Category-agnostic pose estimation (CAPE) aims to locate keypoints on objects of any type using a single model, overcoming the limitations of category-specific pose estimation methods. Current research focuses on developing efficient and accurate models, often employing transformer-based architectures, neural processes, or graph neural networks to handle diverse object shapes and appearances, sometimes incorporating textual descriptions or geometric features. This research is significant because it enables more robust and versatile applications in robotics, augmented reality, and other fields requiring object pose understanding beyond pre-defined categories. The development of large-scale, multi-modal datasets is also a key area of advancement, facilitating the training and evaluation of these category-agnostic models.