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We\ndemonstrate how this enhances the method by allowing high-resolution controlled\nstylisation and helps to alleviate common failure cases such as applying ground\ntextures to sky regions. Furthermore, by decomposing style into these\nperceptual factors we enable the combination of style information from multiple\nsources to generate new, perceptually appealing styles from existing ones. We\nalso describe how these methods can be used to more efficiently produce large\nsize, high-quality stylisation. Finally we show how the introduced control\nmeasures can be applied in recent methods for Fast Neural Style Transfer.","url_abs":"http://arxiv.org/abs/1611.07865v2","url_pdf":"http://arxiv.org/pdf/1611.07865v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"controlling-perceptual-factors-in-neural","repo_url":"https://github.com/leongatys/NeuralImageSynthesis","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok"}},{"paper_slug":"controlling-perceptual-factors-in-neural","repo_url":"https://github.com/Garfield35/Doodle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"controlling-perceptual-factors-in-neural","repo_url":"https://github.com/ProGamerGov/neural-style-pt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"controlling-perceptual-factors-in-neural","repo_url":"https://github.com/cal-app/slow-NST","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"controlling-perceptual-factors-in-neural","repo_url":"https://github.com/dstein64/pastiche","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"controlling-perceptual-factors-in-neural","repo_url":"https://github.com/leongatys/PytorchNeuralStyleTransfer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-manipulation","task_name":"Image Manipulation"},{"task_slug":"style-transfer","task_name":"Style Transfer"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1611.07865","atlas_url":"https://app.syntology.ai/?focus=1611.07865","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.07865"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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