Semantic Pyramid for Image Generation
Assaf Shocher, Yossi Gandelsman, Inbar Mosseri, Michal Yarom, Michal Irani, William T. Freeman, Tali Dekel
摘要
Semantic Pyramid for Image Generation. We introduce a new image generative model that is designed and trained to leverage the hierarchical space of deep-features learned by a pre-trained classification network. Our model provides a unified versatile framework for various image generation and manipulation tasks, including: (a) generating images with a controllable extent of semantic similarity to a reference image, obtained by reconstructing images from different layers of a classification model; (b) generating realistic image samples from unnatural reference image such as line drawings; (c) semantically compositing different images, and (d) controlling the semantic content of an image by enforcing a new, modified class label.
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引用它的顶会 Paper19
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