Hybrid Neural Field

Hybrid neural fields represent a powerful approach to representing and manipulating 3D data by combining the strengths of neural networks with explicit geometric representations. Current research focuses on improving the accuracy of derivative calculations within these fields, developing efficient architectures like tri-plane structures and factored representations to enhance performance and interpretability, and applying them to diverse tasks such as fluid simulation, rendering, and 3D human head modeling. These advancements are leading to more accurate and efficient solutions in various fields, including computer graphics, scientific simulation, and computer vision.