Neural Parametric Model

Neural parametric models represent data using learned parameters within a defined structure, aiming to achieve efficient and effective representation and manipulation of complex data, such as 3D shapes, human bodies, and scenes. Current research focuses on developing novel architectures, including those based on neural implicit functions and Gaussian representations, to improve the disentanglement of latent factors (e.g., identity, pose, clothing) and handle high-dimensional data with varying data types. These models find applications in diverse fields, including computer vision (3D reconstruction, animation), and signal processing (speech prediction), offering improvements in accuracy, efficiency, and the ability to handle limited data.