Image morphing without reference points by optimizing warp maps via gradient descent.
This project introduces a “differentiable morphing” algorithm that can smoothly transition between any two images without requiring manual reference points or landmarks. Unlike traditional generative models that learn a distribution from a dataset, this approach uses a neural network as a temporary functional mapping to solve a specific optimization problem for a single pair of images.
The algorithm finds a set of maps that transform the source image into the target image.
By interpolating the strength of these maps, the system produces a smooth, seamless animation where features transform fluidly from one state to another.
Status: Completed Experiment