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Semantic Draw Engineering for Text-to-Image Creation

2023-12-23 05:35:15
Yang Li, Huaqiang Jiang, Yangkai Wu

Abstract

Text-to-image generation is conducted through Generative Adversarial Networks (GANs) or transformer models. However, the current challenge lies in accurately generating images based on textual descriptions, especially in scenarios where the content and theme of the target image are ambiguous. In this paper, we propose a method that utilizes artificial intelligence models for thematic creativity, followed by a classification modeling of the actual painting process. The method involves converting all visual elements into quantifiable data structures before creating images. We evaluate the effectiveness of this approach in terms of semantic accuracy, image reproducibility, and computational efficiency, in comparison with existing image generation algorithms.

Abstract (translated)

文本到图像生成是通过生成对抗网络(GANs)或Transformer模型进行的。然而,目前的挑战在于根据文本描述准确生成图像,尤其是在目标图像的内容和主题不明确的情况下。在本文中,我们提出了一种利用人工智能模型进行主题创意的方法,并对其进行了分类建模实际绘画过程。该方法在创建图像之前将所有视觉元素转换为可量化的数据结构。我们评估了这种方法在语义准确性、图像可重复性和计算效率方面的有效性,与现有的图像生成算法进行了比较。

URL

https://arxiv.org/abs/2401.04116

PDF

https://arxiv.org/pdf/2401.04116.pdf


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