## 3 Results
### AIGC Input-Output Classification
#### 3.3.6 Approaches for Visual Content Generation
For face aging, there are models like Age-cGAN [135], conditional adversarial autoencoders (CAAE) [122], and identity-preserved conditional generative adversarial networks (IPCGANs) [136]. Age-cGAN [135] employs an architecture that includes a generator network responsible for generating aged faces based on input images and a discriminator network that provides feedback on the realism and age progression of the generated faces. IPCGANs [136] also adopt a similar architecture, with a focus on preserving the identity of the person while generating the hypothetical future appearance. By incorporating conditional inputs and adversarial training, these models generate realistic and convincing aged face images. Furthermore, image segmentation plays a vital role in various domains, particularly in medical image analysis. SegAN [100], a model combining GANs and convolutional neural networks (CNNs), excels in accurately segmenting objects or regions within medical images. By leveraging its generator network for producing segmentation maps and discriminator network for feedback, SegAN contributes to advancements in medical imaging and diagnosis. Boundary equilibrium GAN (BEGAN) [139] focuses on generating high-quality face samples at a resolution of 128 \(\times\) 128 pixels. It employs an architecture that achieves equilibrium between generator and discriminator networks, enabling stable training and the synthesis of visually appealing facial images. To tackle the challenge of converting low-resolution images to high-resolution, two powerful techniques come to the forefront: the multi-level densely connected super-resolution network (mDCSRN) [137] and the super resolution GAN (SRGAN). The mDCSRN takes a unique approach by employing a densely connected architecture with skip connections. By progressively upsampling the input image through multiple levels, it effectively enhances image details while ensuring the preservation and propagation of important image features. On the other hand, SRGAN [90] adopts a different strategy by combining a generator network and a discriminator network. The generator network utilizes deep residual blocks to extract and reconstruct high-frequency image details, resulting in remarkable resolution enhancement. Lastly, domain transfer networks (DTN) [89] specialize in generating images in a target domain based on different source domains. These models facilitate the seamless translation of visual content across domains, enabling applications such as style transfer, image synthesis, and data augmentation.Exploring these image-to-image techniques not only unlocks new possibilities for image manipulation and transformation but also drives advancements in the field of computer vision. These models and tasks provide invaluable tools for tasks such as image editing, deblurring, resolution enhancement, style transfer, domain transfer, and image segmentation, ultimately contributing to the progress and innovation of image processing and synthesis. Text-to-Video  
Text-to-image (T2I) models have achieved remarkable progress in generating visual content based on text descriptions. Building upon this advancement, researchers have now turned their attention to text-to-video (T2V) generation. In the early works of this field, one notable technique that emerged was temporal GANs conditioning on captions (TGANs-C). TGANs-C, based on generative adversarial networks (GANs), focused on generating video sequences from textual descriptions. Expanding to TGANs-C [172], subsequent research has led to the development of various diffusion-based models for text-to-video generation. One such technique is \"Make-A-Video\" by Meta AI, which directly extends the diffusion-based T2I advancements to T2V. This approach leverages paired text-image data to capture the visual appearance and descriptions of the world, while utilizing unsupervised video footage to understand motion dynamics. In the \"Make-A-Video\" [159] technique, the text input undergoes processing using a decoder to create image embeddings.