Learning Hierarchical Semantic Image Manipulation through Structured Representations

TL;DR

This paper introduces a hierarchical semantic image manipulation framework that uses structured semantic layouts for more precise and flexible image editing, outperforming previous methods in quality and controllability.

Contribution

The work presents a novel hierarchical approach employing structured semantic layouts for object-level image manipulation, enabling more accurate and flexible editing capabilities.

Findings

Outperforms existing models in qualitative and quantitative evaluations.

Enables object-level manipulation such as adding, removing, and moving objects.

Demonstrates applications in interactive editing and data-driven image manipulation.

Abstract

Understanding, reasoning, and manipulating semantic concepts of images have been a fundamental research problem for decades. Previous work mainly focused on direct manipulation on natural image manifold through color strokes, key-points, textures, and holes-to-fill. In this work, we present a novel hierarchical framework for semantic image manipulation. Key to our hierarchical framework is that we employ a structured semantic layout as our intermediate representation for manipulation. Initialized with coarse-level bounding boxes, our structure generator first creates pixel-wise semantic layout capturing the object shape, object-object interactions, and object-scene relations. Then our image generator fills in the pixel-level textures guided by the semantic layout. Such framework allows a user to manipulate images at object-level by adding, removing, and moving one bounding box at a time. Experimental evaluations demonstrate the advantages of the hierarchical manipulation framework over existing image generation and context hole-filing models, both qualitatively and quantitatively. Benefits of the hierarchical framework are further demonstrated in applications such as semantic object manipulation, interactive image editing, and data-driven image manipulation.