Enhancing Multi-Region Stylization with Interior-Guided Boundary Repair
We propose Interior-Guided Boundary Repair (IGBR), a lightweight and model-agnostic method that improves boundary handling in multi-region stylization.
ProofPaper ↗
Key points
- Region-based neural style transfer enables fine-grained artistic control by allowing independent stylization of semantic image regions.
- IGBR repairs boundary pixels using interior-guided propagation and applies inward, distance-based blending restricted to object-background boundaries, preventing inter-object style leakage.
- The method is derived from a region-wise constrained formulation with a closed-form solution and can be seamlessly integrated into existing stylization pipelines without retraining.
- To evaluate efficiency of our IGBR, we introduce quantitative metrics that measure boundary consistency, gradient artifacts, inter-object leakage, and interior preservation without requiring annotated stylized images.
Sources (1)
- [1]Enhancing Multi-Region Stylization with Interior-Guided Boundary RepairarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 09:03 AM
We propose Interior-Guided Boundary Repair (IGBR), a lightweight and model-agnostic method that improves boundary handling in multi-region stylization.
Region-based neural style transfer enables fine-grained artistic control by allowing independent stylization of semantic image regions.
Extractive summary: sentences quoted from the sources.