Deformation Prior

Deformation priors are statistical models representing the expected range of shapes and deformations of objects, crucial for solving ill-posed problems in computer vision and graphics, such as 3D object reconstruction and pose estimation. Current research focuses on learning these priors from data, often using neural networks (including transformers) and Gaussian processes, to improve accuracy and efficiency, particularly for complex non-rigid deformations like those found in clothing or articulated objects. These advancements enable more robust and realistic modeling of deformable objects, impacting applications ranging from virtual clothing design to robotic manipulation and medical image analysis. The shift towards prior-free or self-supervised methods highlights a growing interest in reducing reliance on large, manually labeled datasets.