Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis

TL;DR

This paper introduces a novel approach for unsupervised 3D controllable image synthesis, enabling disentanglement of 3D factors and consistent scene generation across viewpoints, bridging the gap between 2D models and 3D understanding.

Contribution

It proposes a new method that models image generation in 3D space, allowing for unsupervised disentanglement of 3D factors and controllable scene synthesis from raw images.

Findings

Model can disentangle 3D factors in multi-object scenes

Enables viewpoint and pose consistent scene synthesis

Outperforms 2D baselines in controllability

Abstract

In recent years, Generative Adversarial Networks have achieved impressive results in photorealistic image synthesis. This progress nurtures hopes that one day the classical rendering pipeline can be replaced by efficient models that are learned directly from images. However, current image synthesis models operate in the 2D domain where disentangling 3D properties such as camera viewpoint or object pose is challenging. Furthermore, they lack an interpretable and controllable representation. Our key hypothesis is that the image generation process should be modeled in 3D space as the physical world surrounding us is intrinsically three-dimensional. We define the new task of 3D controllable image synthesis and propose an approach for solving it by reasoning both in 3D space and in the 2D image domain. We demonstrate that our model is able to disentangle latent 3D factors of simple multi-object scenes in an unsupervised fashion from raw images. Compared to pure 2D baselines, it allows for synthesizing scenes that are consistent wrt. changes in viewpoint or object pose. We further evaluate various 3D representations in terms of their usefulness for this challenging task.