## 4 Discussion
### Input-Output Formats for Prescribed Tasks
These include text-guided image manipulation through methods like LDEdit [49] and StyleCLIP [98], text-based image synthesis with SISGAN [163], and diffusion-based approaches like DIFFEDIT [164] and DiffusionCLIP [131]. Additionally, there are tasks that involve combining images and text for specific purposes. SteganGAN focuses on generating steganographic images, where messages can be hidden within the image itself. This technique can be useful for secure communication or embedding information in visual content. These techniques enable users to perform tasks such as text-driven image editing, steganography, realistic image synthesis, and text-based image manipulation. Generative AI models have demonstrated their capabilities in various tasks related to molecule generation and design. Text-based approaches like MoI75 [58] and Text2 Mol [60] allow for the de novo generation of molecules and the retrieval of molecular structures using textual descriptions. These techniques offer several advantages, including accelerated molecule design, molecule captioning, and efficient exploration of chemical space. Furthermore, methods such as Mol-CycleGAN [125], JT-VAE [57], and MoFlow [76] enable the design of molecules with desired properties by leveraging chemical properties and molecular graphs. These advancements have promising applications in the fields of drug discovery. Generative AI models have made significant strides in the field of 3D content generation based on textual input. These models demonstrate the ability to generate various forms of 3D content guided by text descriptions. Magic3D [103] allows for the detailed synthesis of 3D images, leveraging textual descriptions as input. AvatarCLIP [66] focuses on text-driven 3D avatars with animations, enabling dynamic and interactive virtual characters. DreamFace [102] takes text guidance to create personalized and animatable 3D faces, offering a high degree of customization. AvatarCraft [55] goes a step further by generating 3D human avatars with specific identities and artistic styles based on text prompts. MotionCLIP [118] explores the generation of 3D motion using text descriptions, allowing for the creation of animated sequences. Progressive structure-conditional GANs (PSGAN) [144] specialize in generating animated characters from text, facilitating the development of diverse and engaging virtual personas. DREAMFUSION [133] stands out in text-guided 3D object generation, providing the ability to generate 3D objects based on textual descriptions. DreamAvatar [165] combines text and shape information to generate 3D avatars with customizable features. These advancements not only enable applications such as 3D image inpainting using the point encoder GAN [161], but also offer the generation of realistic human poses using GAN-Poser [51], opening up new possibilities in virtual environments. Generative AI models, including table-GAN [52], TGAN [94], and CTGAN [83], provide the ability to create synthetic tabular datasets that closely resemble the original data. These models offer valuable resources for tasks such as data augmentation, algorithm testing, and privacy preservation. By leveraging these generative models, researchers and practitioners can generate datasets with similar statistical properties as the original data, which can be used for training machine learning models, conducting simulations, and performing various data-driven analyses. This empowers the exploration and development of novel solutions in fields that rely on tabular data, fostering advancements in machine learning, data science, and decision-making processes. In the broader context of generative AI techniques, our discussion encompasses various aspects, including the transformation of unstructured text into structured knowledge graphs through approaches like Grapher [117] for text-to-knowledge-graph conversion. Additionally, we explore the conversion of knowledge graphs, represented as RDF triples, into coherent text using techniques such as GTR-LSTM [129] for knowledge-graph-to-text conversion.