素描
计算机科学
人工智能
计算机视觉
草图识别
图像(数学)
对象(语法)
代表(政治)
计算机图形学(图像)
编码器
图像编辑
算法
手势识别
手势
政治
政治学
法学
操作系统
作者
Zhenbei Wu,Haoge Deng,Qiang Wang,Di Kong,Jie Yang,Yonggang Qi
标识
DOI:10.1109/icme55011.2023.00357
摘要
Sketch is an abstract visual representation that can be recovered as natural photographs in the human mind. Many researchers are drawn to work on translating abstract sketches to natural photographs. Since conventional sketch-to-image models are designed to generate images with a single object as the subject, generating scene image with multiple classes of objects is a tricky problem. To tackle this challenge, we propose the first scene sketch-to-image generation method based on diffusion models. Our model uses an encoder to summarize the contour and class features of the scene sketch into a latent variable, and a decoder to reconstruct scene images from it. In scene sketch-to-image generation tasks, our method outperforms the state-of-the-art methods. Experiments also show that our model beats other methods in zero-shot general sketch-to-image generation. It demonstrates our model’s potential for full-domain image generation.
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