Learning Hierarchical Semantic Image Manipulation through Structured Representations

Seunghoon Hong, Xinchen Yan, Thomas S. Huang, Honglak Lee

Neural Information Processing Systems 

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.

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