SketchPatch: Sketch Stylization via Seamless Patch-level Synthesis
원문 초록
The paradigm of image-to-image translation is leveraged for the benefit of sketch stylization via transfer of geometric textural details. 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. 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. 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. 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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
LBM: Latent Bridge Matching for Fast Image-to-Image Translation
Cora: Correspondence-aware image editing using few step diffusion
Unpaired Image-to-Image Translation for Segmentation and Signal Unmixing
Paired and Unpaired Image to Image Translation using Generative Adversarial Networks
Image-to-Image Translation with Diffusion Transformers and CLIP-Based Image Conditioning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.