First order model

First order model

First order model

For training, we employ a large collection of video sequences containing objects of the same object category. Our model is trained to reconstruct the training videos by combining a single frame and a learned latent representation of the motion in the video. Observing frame pairs (source and driving), each extracted from the same video, it learns to encode motion as a combination of motion-specific keypoint displacements and local affine transformations. At test time we apply our model to pairs composed of the source image and of each frame of the driving video and perform image animation of the source object.

Source: aliaksandrsiarohin.github.io/first-order-model-website/