Adversarial-Inverse-Graphics-Networks-for-Faces

Abstract

Researchers have developed excellent feed-forward models that learn to map images to desired outputs, such as to the images’ latent factors, or to other images, using supervised learning. Learning such mappings from unlabelled data, or improving upon supervised models by exploiting unlabelled data, remains elusive. We argue that there are two important parts to learning without annotations: (i) matching the predictions to the input observations, and (ii) matching the predictions to known priors. We propose Adversarial Inverse Graphics networks (AIGNs): weakly supervised neural network models that combine feedback from rendering their predictions, with distribution matching between their predictions and a collection of ground-truth factors. We apply AIGNs to 3D human pose estimation and 3D structure and egomotion estimation, and outperform models supervised by only paired annotations. We further apply AIGNs to facial image transformation using super-resolution and inpainting renderers, while deliberately adding biases in the ground-truth datasets. Our model seamlessly incorporates such biases, rendering input faces towards young, old, feminine, masculine or Tom Cruiselike equivalents (depending on the chosen bias), or adding lip and nose augmentations while inpainting concealed lips and noses.

Renderer

The most important part of an AIGN network is its Renderer. The renderer in this paper is a non parameteric decoder which needs to be hand engineered dependent on the use-case. As mentioned in the paper the renderer used by the authors for face translation is a 4:1 Average Pooling Network.

For few specific cases I have extended the renderer to a Gray Scale Average Pooling Network, which results in good results for Human to Avatar and Black Hair to Blonde Hair as the previous renderer devalues color change

Learning Transistion

Old to Young

Results

Information