Differentiable Stroke Planning with Dual Parameterization for Efficient and High-Fidelity Painting Creation
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911564496371712 |
|---|---|
| author | Liu, Jinfan Zhang, Wuze Hu, Zhangli Zhao, Zhehan Chen, Ye Ni, Bingbing |
| author_facet | Liu, Jinfan Zhang, Wuze Hu, Zhangli Zhao, Zhehan Chen, Ye Ni, Bingbing |
| contents | In stroke-based rendering, search methods often get trapped in local minima due to discrete stroke placement, while differentiable optimizers lack structural awareness and produce unstructured layouts. To bridge this gap, we propose a dual representation that couples discrete polylines with continuous Bézier control points via a bidirectional mapping mechanism. This enables collaborative optimization: local gradients refine global stroke structures, while content-aware stroke proposals help escape poor local optima. Our representation further supports Gaussian-splatting-inspired initialization, enabling highly parallel stroke optimization across the image. Experiments show that our approach reduces the number of strokes by 30-50%, achieves more structurally coherent layouts, and improves reconstruction quality, while cutting optimization time by 30-40% compared to existing differentiable vectorization methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02752 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Differentiable Stroke Planning with Dual Parameterization for Efficient and High-Fidelity Painting Creation Liu, Jinfan Zhang, Wuze Hu, Zhangli Zhao, Zhehan Chen, Ye Ni, Bingbing Computer Vision and Pattern Recognition In stroke-based rendering, search methods often get trapped in local minima due to discrete stroke placement, while differentiable optimizers lack structural awareness and produce unstructured layouts. To bridge this gap, we propose a dual representation that couples discrete polylines with continuous Bézier control points via a bidirectional mapping mechanism. This enables collaborative optimization: local gradients refine global stroke structures, while content-aware stroke proposals help escape poor local optima. Our representation further supports Gaussian-splatting-inspired initialization, enabling highly parallel stroke optimization across the image. Experiments show that our approach reduces the number of strokes by 30-50%, achieves more structurally coherent layouts, and improves reconstruction quality, while cutting optimization time by 30-40% compared to existing differentiable vectorization methods. |
| title | Differentiable Stroke Planning with Dual Parameterization for Efficient and High-Fidelity Painting Creation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.02752 |