Parametric Representation

Parametric representation focuses on efficiently encoding data, particularly complex structures like 3D shapes and human faces, using a reduced set of parameters. Current research emphasizes learning these parameters through neural networks, employing architectures like implicit neural representations (INRs), Bayesian flow networks, and hybrid models combining explicit and implicit approaches. This field is crucial for advancing applications in computer graphics, robotics, and medical imaging, enabling compact storage, efficient processing, and improved control over generated content. The development of more efficient and expressive parametric representations is driving progress in various fields by improving the accuracy and speed of data processing and generation.