# Text-Guided Customizable Image Synthesis and Manipulation
## 4 Experimental Results
### Qualitative Results  
**Customizable Image Synthesis Results. ** The customizable image synthesis results on the CUB and MS-COCO datasets are shown in Figure 4. The input content included two parts: one was the text information used to determine the composite content, and the other was the contour map used to determine the composite object's shape information. The synthesized image results were not only in accord with the text's semantic information but also maintained the basic contour shape, which reflects the effectiveness of our method in customizing the synthetic image. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**CUB**} & \multicolumn{2}{c}{**MS COCO**} \\ \cline{2-5}  & **Train** & **Test** & **Train** & **Test** \\ \hline Number of images & 8855 & 2933 & 82,783 & 40,504 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The basic information for the CUB and MS COCO datasets. Figure 3: The processed contour results based on the original image are shown above. **Text-Guided Image Synthesis and Manipulation. ** The customized image synthesis and manipulation results based on text information are shown in Figure 5. Based on the input text and contour information, our method can generate the corresponding image results. It then allows continuous input of text information for modifying previously synthesized content. In comparison with directly inputting a long text description in the customization stage to synthesize the corresponding results, Figure 5 shows that our method can input simple text attributes to modify the previous synthesized image's content in the image manipulation stage, which shows the robustness of our method in manipulating the synthesized image with a few significant words. **Comparison with T2I Methods. ** The qualitative comparison results between our method and the current T2I methods are shown in Figure 6. From the bird synthesis results, the current T2I method, AttnGAN [5], could synthesize corresponding image results based on text descriptions. However, the quality of their synthesized results was mediocre. On the other hand, it can be clearly seen that the shapes of their synthesized results were different. Some shapes had poor authenticity, which made the synthetic results less authentic. In contrast, our results kept the contour's shape information, which guaranteed the authenticity of the generated results. Furthermore, they were consistent with the text's semantic information, which demonstrates the effectiveness of the proposed customized synthesis method under the text's guidance. The results of the complex image further reflect the superiority of our proposed method. Compared with the results synthesized by AttnGAN, the synthesized results of our method subjectively had excellent authenticity. **Comparison with Custom Synthesis Methods. ** The comparison results with other customized synthesis methods are shown in Figure 7. The first two lines show the input for customizable image synthesis, including the text description and contour. The next two lines reveal the results of CustomizableGAN [16] and our method. Overall, both methods could synthesize customized results based on text and contour information. However, the results of our method were better in terms of authenticity and clarity. In terms of details, such as eyes and textures, our results had superior performance. Figure 4: The customized synthesis results based on text and contour are shown above. The shape of the synthesized image is determined by the input contour, and the detailed content is determined by the text information. Figure 5: The above figure shows the results of image synthesis and manipulation based on text guidance. First, the image content was generated based on the text and contour information, and then the content of the synthesized image could be modified continuously by inputting the text of the object’s attributes.