Segmentation-Based Parametric Painting
We introduce a novel image-to-painting method that facilitates the creation of large-scale, high-fidelity paintings with human-like quality and stylistic variation. To process large images and gain control over the painting process, we introduce a segmentation-based painting process and a dynamic attention map approach inspired by human painting strategies, allowing optimization of brush strokes to proceed in batches over different image regions, thereby capturing both large-scale structure and fine details, while also allowing stylistic control over detail. Our optimized batch processing and patch-based loss framework enable efficient handling of large canvases, ensuring our painted outputs are both aesthetically compelling and functionally superior as compared to previous methods, as confirmed by rigorous evaluations. Code available at: https://github.com/manuelladron/semantic\_based\_painting.git
Code (1)
Tasks
SegmentationSimilar Papers 제목 키워드 기반
Semi-parametric Image Inpainting
This paper introduces a semi-parametric approach to image inpainting for irregular holes. The nonparametric part consists of an external image database. During test time database is used to retrieve a supplementary image…
Image InpaintingDepth-SIMS: Semi-Parametric Image and Depth Synthesis
In this paper we present a compositing image synthesis method that generates RGB canvases with well aligned segmentation maps and sparse depth maps, coupled with an in-painting network that transforms the RGB canvases in…
Depth CompletionImage GenerationSegmentationSemantic SegmentationSemantic Segmentation in Art Paintings
Semantic segmentation is a difficult task even when trained in a supervised manner on photographs. In this paper, we tackle the problem of semantic segmentation of artistic paintings, an even more challenging task becaus…
Domain AdaptationSegmentationSemantic SegmentationStyle TransferSPG-Net: Segmentation Prediction and Guidance Network for Image Inpainting
In this paper, we focus on image inpainting task, aiming at recovering the missing area of an incomplete image given the context information. Recent development in deep generative models enables an efficient end-to-end f…
Image GenerationImage InpaintingInteractive SegmentationSegmentation+1Automatic Semantic Content Removal by Learning to Neglect
We introduce a new system for automatic image content removal and inpainting. Unlike traditional inpainting algorithms, which require advance knowledge of the region to be filled in, our system automatically detects the …
DecoderSegmentation