# Text-Guided Customizable Image Synthesis and Manipulation
## 1 Introduction  
Painting is a visual art but also a man-made art. With the rise of artificial intelligence, scientists have been committed to making machines intelligent bodies like humans and achieving many human behaviors, such as face recognition, text generation, and painting. One of the most difficult behaviors to achieve is image synthesis. Unlike straightforward information, such as category labels and text, images contain a wealth of information, making them more difficult for machines to understand. Therefore, image synthesis is a very challenging task for machines. In recent years, machines have been performing better and better in image synthesis tasks due to the emergence of deep learning. Some outstanding models, such as variational autoencoders [1] and autoregressive models [2], can already generate attractive image synthesis results. Recently, the emergence of generative adversarial networks (GANs) [3] is a milestone in image synthesis. Inspired by the GAN, many works have been able to synthesize highly realistic image results. However, the original input of a GAN only contains noise vectors derived from Gaussian distribution or uniform distribution, making the synthesis process of the GAN-based image synthesis model artificially uncontrollable, leading to its poor practicality. To improve the practicability of the GAN model, the conditional generative adversarial network (CGAN) [4] was proposed. A CGAN can realize the control of the composite image category and related attributes by introducing additional category labels and attribute information. For example, given a category label \"bird\", the model can synthesize an image with birds. However, it is difficult for this method to realize complicated image synthesisdue to the small amount of information contained. On the other hand, category labels or attributes do not conform to human input habits, resulting in the corresponding model not having good applicability. To address this issue of CGANs, text-to-image synthesis (T2I) was proposed. A text description contains rich information such as content and colors and is intuitive to humans. Therefore, using text descriptions to generate images has recently attracted the attention of many researchers. Many research methods have been proposed in this field, and they have achieved stunning image synthesis effects. Although the research on text-based image synthesis has resulted in impressive achievements, there still remain several problems. Concretely, the text information cannot determine the specific shape of the synthesized image, resulting in some inferior results that may be included in the synthesized images (as shown in Figure 1a). On the other hand, T2I methods can only generate the corresponding image based on the input text, whereas they cannot use the new text description to modify the synthesized image and obtain satisfactory results. These problems lower the practicability of current T2I works. To cope with these issues, we propose a text-guided customizable image synthesis and manipulation method with two modules: (1) a customizable image synthesis module, which leverages the text description and contour information to synthesize the corresponding image result, and (2) an image manipulation module, in which new text can be used to modify the previously synthesized image content to achieve a satisfactory result. The text description and contour information in the two modules allow manual input and conform to people's input habits. Thus, our method has high flexibility and practicability. The main contributions of our work are as follows:  
* We propose an effective and novel method that can perform customized image synthesis based on text and contour information and then utilize new text to modify the customized image content. * Compared with recent T2I methods, our proposed method can basically ensure the synthesized image's authenticity because the contour information determines the basic shape.