Neural Deformable

Neural deformable models represent a powerful approach to reconstructing and modeling complex, dynamic 3D shapes, particularly in challenging scenarios with sparse or incomplete data. Current research focuses on developing novel architectures, such as deformable primitive fields and neural diffeomorphic point flows, to accurately capture both global and fine-grained shape details, often leveraging parameterized geometric primitives or underlying skeletal models for improved efficiency and interpretability. These methods show promise in diverse applications, including 3D object reconstruction, medical image analysis (e.g., cardiac modeling), and human body digitization for animation and virtual reality, offering improvements over traditional techniques. The ability to learn and represent complex deformations directly from data is driving significant advancements in these fields.