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Semantic-aware diffusion model for sketch colorization
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AI
DOI：10.1080/13682199.2025.2565106.png)
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Abstract
En 中文
Sketch colorization is a classical topic in the fields of computer vision and multimedia. The challenge of the task lies in the semantic consistency keeping during the colorization, which poses a challenge to the precise semantic parsing and colour matching capabilities of the colorization. This paper proposes a semantic-aware colour generation diffusion model to implement automatic colouring of sketches with complex structures. It consists of two parts: semantic-aware colour initialization based on a cross-attention mechanism and colour generation based on diffusion denoising. To obtain semantic awareness, we designs a cross-attention mechanism module to analyze the semantic features of input sketch and match them with reference colour images. According to the matching relationship, a semantically consistent colour initialization can be established for the sketch. Based on the initial result, we establish a colouring diffusion model module, which performs noise addition and denoising to further optimize colour distribution. The proposed model fully considers the semantic constraint requirements, ensuring that the generated colours have good semantic consistency and meet specific colour styles. Experiments show that our method can achieve high-quality colours with semantic correspondence, which is beneficial for applications such as cultural relic restoration and digital media.
Keywords:
Sketch colorization
semantic consistency
diffusion model
attention
Journal
I
IF:
1.1
Papers:
59
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0