Smooth Interpolation

Smooth interpolation aims to generate intermediate data points between known data samples, creating a continuous and natural transition. Current research focuses on developing algorithms that achieve smooth interpolation in complex, high-dimensional spaces, such as those encountered in image morphing, 3D scene reconstruction, and trajectory inference, often employing diffusion models, generative adversarial networks, or kernel methods. These advancements are improving the quality of generated data and enabling applications in diverse fields, including medical imaging, computer graphics, and machine learning, by facilitating more realistic simulations and improved model interpretability. The development of more efficient and robust interpolation techniques is a key area of ongoing investigation.