SketchyCOCO: Image Generation from Freehand Scene Sketches

• URL: http://arxiv.org/abs/2003.02683v5
• Date: Tue, 7 Apr 2020 10:15:39 GMT
• Title: SketchyCOCO: Image Generation from Freehand Scene Sketches
• Authors: Chengying Gao, Qi Liu, Qi Xu, Limin Wang, Jianzhuang Liu, Changqing Zou
• Abstract summary: We introduce the first method for automatic image generation from scene-level freehand sketches. Key contribution is an attribute vector bridged Geneversarative Adrial Network called EdgeGAN. We have built a large-scale composite dataset called SketchyCOCO to support and evaluate the solution.
• Score: 71.85577739612579
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce the first method for automatic image generation from scene-level freehand sketches. Our model allows for controllable image generation by specifying the synthesis goal via freehand sketches. The key contribution is an attribute vector bridged Generative Adversarial Network called EdgeGAN, which supports high visual-quality object-level image content generation without using freehand sketches as training data. We have built a large-scale composite dataset called SketchyCOCO to support and evaluate the solution. We validate our approach on the tasks of both object-level and scene-level image generation on SketchyCOCO. Through quantitative, qualitative results, human evaluation and ablation studies, we demonstrate the method's capacity to generate realistic complex scene-level images from various freehand sketches.

Related papers

• SketchTriplet: Self-Supervised Scenarized Sketch-Text-Image Triplet Generation [6.39528707908268]
  There continues to be a lack of large-scale paired datasets for scene sketches. We propose a self-supervised method for scene sketch generation that does not rely on any existing scene sketch. We contribute a large-scale dataset centered around scene sketches, comprising highly semantically consistent "text-sketch-image" triplets.
  arXiv  Detail & Related papers  (2024-05-29T06:43:49Z)
• DiffSketching: Sketch Control Image Synthesis with Diffusion Models [10.172753521953386]
  Deep learning models for sketch-to-image synthesis need to overcome the distorted input sketch without visual details. Our model matches sketches through the cross domain constraints, and uses a classifier to guide the image synthesis more accurately. Our model can beat GAN-based method in terms of generation quality and human evaluation, and does not rely on massive sketch-image datasets.
  arXiv  Detail & Related papers  (2023-05-30T07:59:23Z)
• I Know What You Draw: Learning Grasp Detection Conditioned on a Few Freehand Sketches [74.63313641583602]
  We propose a method to generate a potential grasp configuration relevant to the sketch-depicted objects. Our model is trained and tested in an end-to-end manner which is easy to be implemented in real-world applications.
  arXiv  Detail & Related papers  (2022-05-09T04:23:36Z)
• FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context [112.07988211268612]
  We advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. Our dataset comprises 10,000 freehand scene vector sketches with per point space-time information by 100 non-expert individuals. We study for the first time the problem of the fine-grained image retrieval from freehand scene sketches and sketch captions.
  arXiv  Detail & Related papers  (2022-03-04T03:00:51Z)
• Self-Supervised Sketch-to-Image Synthesis [21.40315235087551]
  We study the exemplar-based sketch-to-image (s2i) synthesis task in a self-supervised learning manner. We first propose an unsupervised method to efficiently synthesize line-sketches for general RGB-only datasets. We then present a self-supervised Auto-Encoder (AE) to decouple the content/style features from sketches and RGB-images, and synthesize images that are both content-faithful to the sketches and style-consistent to the RGB-images.
  arXiv  Detail & Related papers  (2020-12-16T22:14:06Z)
• Cross-Modal Hierarchical Modelling for Fine-Grained Sketch Based Image Retrieval [147.24102408745247]
  We study a further trait of sketches that has been overlooked to date, that is, they are hierarchical in terms of the levels of detail. In this paper, we design a novel network that is capable of cultivating sketch-specific hierarchies and exploiting them to match sketch with photo at corresponding hierarchical levels.
  arXiv  Detail & Related papers  (2020-07-29T20:50:25Z)
• Sketch-Guided Scenery Image Outpainting [83.6612152173028]
  We propose an encoder-decoder based network to conduct sketch-guided outpainting. We apply a holistic alignment module to make the synthesized part be similar to the real one from the global view. Second, we reversely produce the sketches from the synthesized part and encourage them be consistent with the ground-truth ones.
  arXiv  Detail & Related papers  (2020-06-17T11:34:36Z)
• Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches [133.01690754567252]
  Sketch-based image editing aims to synthesize and modify photos based on the structural information provided by the human-drawn sketches. Deep Plastic Surgery is a novel, robust and controllable image editing framework that allows users to interactively edit images using hand-drawn sketch inputs.
  arXiv  Detail & Related papers  (2020-01-09T08:57:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.