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Quadratic Bezier Splatting with Structural Initialization for Efficient Painting Rendering
JL

Jinfan Liu, Xuhan Zhan, Mengqin Zhao, Guangyi Deng, Wuze Zhang, Bingbing Ni

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ResearcharXiv cs.CV

Quadratic Bezier Splatting with Structural Initialization for Efficient Painting Rendering

arXiv:2611.10713v1 Announce Type: new Abstract: Stroke-based rendering is often optimized as a pixel-level reconstruction task. This over-reliance on global error reduction not only degrades strokes into fragmented curves due to a lack of structured planning, but also allows locally defective primitives to be concealed by overlapping layers. To address these issues, we introduce a model-guided framework for stroke-audited rendering. Our framework uses Quadratic Bezier Splatting to analytically model continuous curved strokes with efficient differentiable alpha compositing. Built on this renderer, a feedforward multi-scale stroke decoder provides a structural initialization with learned stroke-scale allocation, while test-time stroke optimization supplies the reconstruction capacity needed for high-fidelity refinement. We further propose a single-stroke audit mechanism that explicitly evaluates local stroke quality, preventing defective primitives from surviving optimization through layer-wise compensation. Compared with existing methods, our approach achieves coherent layouts, improves PSNR by up to 5.46 dB, yields a 16% more compact representation, and accelerates combined renderer forward and backward computation by a factor of 220.3.

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This story was published by arXiv cs.CV and written by Jinfan Liu, Xuhan Zhan, Mengqin Zhao, Guangyi Deng, Wuze Zhang, Bingbing Ni. SyncAI.news shows a preview; the complete article is on the publisher's site.

Read the full story on arxiv.org

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