# Shapes and Context: In-the-Wild Image Synthesis & Manipulation

## TL;DR

This paper presents a non-learning, data-driven method for synthesizing high-resolution, in-the-wild images from semantic labels, enabling diverse, user-controllable image generation without training limitations.

## Contribution

It introduces a simple, exemplar-based approach that avoids learning, allowing high-resolution synthesis and diverse outputs, surpassing recent learning-based methods in flexibility and quality.

## Key findings

- Outperforms learning-based methods on COCO dataset metrics
- Supports arbitrarily high-resolution image synthesis
- Enables interactive, user-driven content creation

## Abstract

We introduce a data-driven approach for interactively synthesizing in-the-wild images from semantic label maps. Our approach is dramatically different from recent work in this space, in that we make use of no learning. Instead, our approach uses simple but classic tools for matching scene context, shapes, and parts to a stored library of exemplars. Though simple, this approach has several notable advantages over recent work: (1) because nothing is learned, it is not limited to specific training data distributions (such as cityscapes, facades, or faces); (2) it can synthesize arbitrarily high-resolution images, limited only by the resolution of the exemplar library; (3) by appropriately composing shapes and parts, it can generate an exponentially large set of viable candidate output images (that can say, be interactively searched by a user). We present results on the diverse COCO dataset, significantly outperforming learning-based approaches on standard image synthesis metrics. Finally, we explore user-interaction and user-controllability, demonstrating that our system can be used as a platform for user-driven content creation.