Shapes and Context: In-the-Wild Image Synthesis & Manipulation
This work addresses the need for flexible, high-resolution image synthesis across diverse domains without being constrained by training data distributions, offering a platform for user-driven content creation.
The paper tackles the problem of synthesizing in-the-wild images from semantic label maps by using a data-driven, learning-free approach that matches scene context, shapes, and parts to an exemplar library, achieving significant outperformance over learning-based methods on standard metrics with the COCO dataset.
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.