Style transfer aims to synthesize an image which inherits the content of one image while preserving a similar style of the other one. The “style” of an image usually refers to its unique feeling conveyed from visual features, which is highly related to the aesthetic effect of the image. Aesthetic effect can be mainly decomposed as two factors: colour and texture. Previous methods like Neural Style Transfer and Colour Transfer have shown strong abilities in transferring colour and texture features. However, such approaches neglect to further disentangle colour and texture, which makes some of unique aesthetic effects designed by human artists hard to express. In this paper, we propose a novel problem called Aesthetic-Aware Image Style Transfer task, which aims to transfer colour and texture separately and independently to manipulate the aesthetic effect of an image. We propose a novel Aesthetic-Aware Model-Optimisation-Based Style Transfer (AAMOBST) model to solve this problem. Specifically, AAMOBST is a multi-reference, two-path model. It uses different reference images to decide desired colour and texture features. It can segregate colour and texture into two distinct paths and transfer them independently. Qualitative and quantitative experiments show that our model can decide colour and texture features separately and is able to keep one of them fixed while changing the other one, which is not applicable for previous methods. Furthermore, on tasks that are applicable for previous methods (such as style transfer, colour-preserved transfer and colour-only transfer), our model shows comparable abilities with other baseline methods.<\/p>\n","protected":false},"excerpt":{"rendered":"
Style transfer aims to synthesize an image which inherits the content of one image while preserving a similar style of the other one. The “style” of an image usually refers to its unique feeling conveyed from visual features, which is highly related to the aesthetic effect of the image. Aesthetic effect can be mainly decomposed […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-746284","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2020-10-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"http:\/\/hcsi.cs.tsinghua.edu.cn\/Paper\/Paper20\/MM20-HUZHIYUAN.pdf","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"Bei Liu","user_id":38889,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Bei Liu"},{"type":"user_nicename","value":"Jianlong Fu","user_id":32260,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Jianlong Fu"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[733501],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":733501,"post_title":"Image\/Video Transformation","post_name":"image-video-transformation","post_type":"msr-project","post_date":"2021-04-20 21:08:11","post_modified":"2021-05-30 23:12:35","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/image-video-transformation\/","post_excerpt":"Image and video have become the language people use to communicate on the Internet. 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