Refining Strokes by Learning Offset Attributes between Strokes for Flexible Sketch Edit at Stroke-Level
Sicong Zang, Tao Sun, Cairong Yan

TL;DR
This paper introduces SketchMod, a method for stroke-level sketch editing that refines source strokes by learning and applying offset attributes for scale, orientation, and position to better align with target sketches, improving flexibility and precision.
Contribution
It proposes a novel stroke refinement technique that learns offset attributes to align source strokes with target sketches, enabling more flexible and accurate sketch editing.
Findings
SketchMod achieves precise stroke alignment.
The method improves flexibility in sketch editing.
Experimental results show superior performance.
Abstract
Sketch edit at stroke-level aims to transplant source strokes onto a target sketch via stroke expansion or replacement, while preserving semantic consistency and visual fidelity with the target sketch. Recent studies addressed it by relocating source strokes at appropriate canvas positions. However, as source strokes could exhibit significant variations in both size and orientation, we may fail to produce plausible sketch editing results by merely repositioning them without further adjustments. For example, anchoring an oversized source stroke onto the target without proper scaling would fail to produce a semantically coherent outcome. In this paper, we propose SketchMod to refine the source stroke through transformation so as to align it with the target sketch's patterns, further realize flexible sketch edit at stroke-level. As the source stroke refinement is governed by the patterns…
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Taxonomy
TopicsInteractive and Immersive Displays · Generative Adversarial Networks and Image Synthesis · Multimodal Machine Learning Applications
