Paper Info
Title
Learning Hierarchical Semantic Image Manipulation through Structured Representations.
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 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.
Year
Venue
Keywords
2018
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018)
object manipulation,bounding box,image manipulation,image generator,direct manipulation
DocType
Volume
ISSN
Conference
31
1049-5258
Citations 
PageRank 
References 
6
0.40
15
Authors
4
Name
Order
Citations
PageRank
Seunghoon Hong189930.34
Xinchen Yan241516.71
Thomas S. Huang3278152618.42
Honglak Lee46247398.39