{"type":"ScholarlyArticle","title":"SketchEdit: Editing Freehand Sketches at the Stroke-Level","authors":[{"name":"Tengjie Li","slug":"tengjie-li","given":"Tengjie","family":"Li","affiliation":"Department of Computer Science and Engineering , Shanghai Jiao Tong University , Shanghai , China"},{"name":"Shikui Tu","slug":"shikui-tu","given":"Shikui","family":"Tu","affiliation":"Department of Computer Science and Engineering , Shanghai Jiao Tong University , Shanghai , China"},{"name":"Lei Xu","slug":"lei-xu","given":"Lei","family":"Xu","affiliation":"Department of Computer Science and Engineering , Shanghai Jiao Tong University , Shanghai , China; Guangdong Institute of Intelligence Science and Technology , Zhuhai , Guangdong 519031 , China"}],"abstract":"Recent sketch synthesis methods have demonstrated the capability of generating lifelike outcomes. However, these methods directly encode the entire sketches making it challenging to decouple the strokes from the sketches and have difficulty in controlling local sketch synthesis, e.g., stroke editing. Besides, the sketch editing task encounters the issue of accurately positioning the edited strokes, because users may not be able to draw on the exact position and the same stroke may appear in various locations in different sketches. We propose SketchEdit to realize flexible editing of sketches at the stroke-level for the first time. To tackle the challenge of decoupling strokes, SketchEdit divides a drawing sequence of a sketch into a series of strokes based on the pen state, aligns the stroke segments to have the same starting position, and learns the embeddings of every stroke by a proposed stroke encoder. Moreover, we overcome the problem of stroke placement via a diffusion process, which progressively generates the locations for the strokes to be synthesized, using the stroke features as the guiding condition. Experiments demonstrate that SketchEdit is effective for stroke-level sketch editing and sketch reconstruction.  The source code is publicly available at https://github.com/CMACH508/SketchEdit/.","doi":"10.24963/ijcai.2024/493","url":"https://ijcai.org/proceedings/2024/493","pdf":"https://ijcai.org/proceedings/2024/0493.pdf","markdown":"https://ijcai.org/proceedings/2024/493.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/493","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":493,"pages":{"first":4461,"last":4469},"keywords":["Computer Vision: CV: Applications","Computer Vision: CV: Image and video synthesis and generation","Machine Learning: ML: Generative models","Computer Vision: CV: Representation learning"],"videos":[],"references":[{"position":1,"raw":"[Alaniz et al., 2022] Stephan Alaniz, Massimiliano Mancini, Anjan Dutta, Diego Marcos, and Zeynep Akata. 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