Scene Designer: a Unified Model for Scene Search and Synthesis from Sketch

TL;DR

Scene Designer introduces a unified model that combines scene search and synthesis from sketches using a graph neural network and Transformer, enabling accurate scene retrieval and coherent layout generation.

Contribution

A novel unified model that jointly learns cross-modal scene search and layout synthesis from sketches, utilizing GNN and Transformer with contrastive learning.

Findings

State-of-the-art sketch-based scene search accuracy

Effective scene layout synthesis from sketches

Unified framework for search and synthesis

Abstract

Scene Designer is a novel method for searching and generating images using free-hand sketches of scene compositions; i.e. drawings that describe both the appearance and relative positions of objects. Our core contribution is a single unified model to learn both a cross-modal search embedding for matching sketched compositions to images, and an object embedding for layout synthesis. We show that a graph neural network (GNN) followed by Transformer under our novel contrastive learning setting is required to allow learning correlations between object type, appearance and arrangement, driving a mask generation module that synthesises coherent scene layouts, whilst also delivering state of the art sketch based visual search of scenes.