SketchTriplet: Self-Supervised Scenarized Sketch-Text-Image Triplet Generation

• URL: http://arxiv.org/abs/2405.18801v1
• Date: Wed, 29 May 2024 06:43:49 GMT
• Title: SketchTriplet: Self-Supervised Scenarized Sketch-Text-Image Triplet Generation
• Authors: Zhenbei Wu, Qiang Wang, Jie Yang,
• Abstract summary: 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.
• Score: 6.39528707908268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The scarcity of free-hand sketch presents a challenging problem. Despite the emergence of some large-scale sketch datasets, these datasets primarily consist of sketches at the single-object level. There continues to be a lack of large-scale paired datasets for scene sketches. In this paper, we propose a self-supervised method for scene sketch generation that does not rely on any existing scene sketch, enabling the transformation of single-object sketches into scene sketches. To accomplish this, we introduce a method for vector sketch captioning and sketch semantic expansion. Additionally, we design a sketch generation network that incorporates a fusion of multi-modal perceptual constraints, suitable for application in zero-shot image-to-sketch downstream task, demonstrating state-of-the-art performance through experimental validation. Finally, leveraging our proposed sketch-to-sketch generation method, we contribute a large-scale dataset centered around scene sketches, comprising highly semantically consistent "text-sketch-image" triplets. Our research confirms that this dataset can significantly enhance the capabilities of existing models in sketch-based image retrieval and sketch-controlled image synthesis tasks. We will make our dataset and code publicly available.

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