# Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision

## TL;DR

This paper introduces Adversarial Inverse Graphics Networks (AIGNs), a weakly supervised framework that learns 2D-to-3D lifting and image translation from unpaired data by combining rendering feedback with distribution matching.

## Contribution

The paper presents a novel weakly supervised neural network model that integrates rendering feedback and distribution matching to learn complex image-to-structure mappings without paired annotations.

## Key findings

- Outperforms supervised models in 3D pose and structure estimation
- Successfully applies to facial transformation with biases
- Handles unpaired data effectively

## 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 Cruise-like equivalents (depending on the chosen bias), or adding lip and nose augmentations while inpainting concealed lips and noses.