Non Rigid Structure From Motion

Non-rigid structure-from-motion (NRSfM) aims to reconstruct three-dimensional shapes and their deformations over time from a sequence of two-dimensional images, a challenging problem due to inherent ambiguities in motion and shape. Current research focuses on improving the accuracy and robustness of reconstruction by incorporating temporal smoothness constraints, spatially-variant deformation models, and leveraging deep learning architectures like sequence-to-sequence networks and diffusion models, often integrating them with classical factorization methods. These advancements are significant for applications such as 3D human pose estimation, dynamic scene modeling, and controllable generative models of non-rigid objects, enabling more accurate and detailed representations of complex movements and deformations.