Shape Change

Shape change research focuses on understanding and modeling how shapes transform over time or across different instances, with applications spanning computer vision, biomedicine, and robotics. Current efforts concentrate on developing algorithms and models, including deep learning architectures like transformers and recurrent neural networks, as well as geometric methods like functional maps and geodesic regression, to accurately represent and predict these changes. This research is significant for improving medical image analysis (e.g., tracking organ deformation, diagnosing diseases), enabling realistic animation and virtual character creation, and advancing the design and control of soft robots. The development of efficient and accurate shape change models is crucial for numerous fields.