3D Shape Sequence

3D shape sequences, representing the temporal evolution of three-dimensional forms, are a focus of intense research across diverse fields. Current efforts concentrate on improving the efficiency of representing and processing these sequences, including developing novel compression techniques leveraging temporal correlations and employing deep learning architectures like transformers and sequence-to-sequence models for tasks such as action recognition and scene flow prediction. These advancements are driving progress in areas such as computer vision, medical imaging (e.g., MRI optimization), and robotics, enabling more efficient data handling and improved analysis of dynamic 3D phenomena.