SketchEdit: Editing Freehand Sketches at the Stroke-Level

SketchEdit: Editing Freehand Sketches at the Stroke-Level

Tengjie Li, Shikui Tu, Lei Xu

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 4461-4469. https://doi.org/10.24963/ijcai.2024/493

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/.
Keywords:
Machine Learning: ML: Generative models
Computer Vision: CV: Applications
Computer Vision: CV: Image and video synthesis and generation 
Computer Vision: CV: Representation learning