![]() Image style transfer using convolutional neural networks. Leon A Gatys, Alexander S Ecker, and Matthias Bethge.Fast patch-based style transfer of arbitrary style. A survey of the state-of-the-art in patch-based synthesis. In European Conference on Computer Vision. The generalized patchmatch correspondence algorithm. Connelly Barnes, Eli Shechtman, Dan B Goldman, and Adam Finkelstein.In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vol. Multi-content gan for few-shot font style transfer. Samaneh Azadi, Matthew Fisher, Vladimir Kim, Zhaowen Wang, Eli Shechtman, and Trevor Darrell.An adversarial addition promotes generalization and robustness to diverse geometries at inference time, forming a simple and effective system for arbitrary sketch stylization, as demonstrated upon a variety of styles and sketches. ![]() Aligned pairs of styled and plain primitives are combined to form input hybrids containing styled elements around the border and plain elements within, and given as input to a seamless translation (ST) generator, whose output patches are expected to reconstruct the fully styled patch. #CALLSIGN PATCH STYLIZER FULL#Operating at the patch level necessitates special consideration of full sketch translation, as individual translation of patches with no regard to neighbors is likely to produce visible seams and artifacts at patch borders. ![]() Lacking the necessary volumes of data for standard training of translation systems, we advocate for operation at the patch level, where a handful of stylized sketches provide ample mining potential for patches featuring basic geometric primitives. The paradigm of image-to-image translation is leveraged for the benefit of sketch stylization via transfer of geometric textural details. ![]()
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