Explicitly disentangling image content from translation and rotation with spatial-VAE

Understand

Given an image dataset, we are often interested in finding data generative factors that encode semantic content independently from pose variables such as rotation and translation.

• However, current disentanglement approaches do not impose any specific structure on the learned latent representations.
• We propose a method for explicitly disentangling image rotation and translation from other unstructured latent factors in a variational autoencoder (VAE) framework.
• By formulating the generative model as a function of the spatial coordinate, we make the reconstruction error differentiable with respect to latent translation and rotation parameters.