# Dynamic Fashion Video Synthesis from Static Imagery
## 2 Background
### Generative Adversarial Networks  
Generative Adversarial Networks (GANs) have emerged as cutting-edge technology for image synthesis and generation, with significant advancements made in recent years. Particularly, StyleGANs have showcased the potential of GANs in generating remarkably realistic photographic images [10; 11]. GANs comprise two neural networks that operate in an adversarial manner, allowing the generator network to produce samples that mimic the underlying dataset while the discriminator network evaluates whether the sample is real or generated [12]. MoCoGAN generates unconditioned videos from noise vectors [13]. The model uses two principles to generate a video--the content vector and motion vector. The content vector specifies the object and appearance of the video, while the motion vector specifies the motion and movement of the video. The framework generates a video by mapping a sequence of random vectors to a sequence of video frames. Each random vector consists of a content part and a motion part. The content part remains fixed, while the motion part is a stochastic process. MoCoGAN has two discriminators, one that differentiates between real and fake images and the other that differentiates between real and fake video stacks. This ensures that the generated video is of good quality and has realistic dynamics. [PERSON] et al. utilised a spatiotemporal convolutional architecture to synthesise a video [14]. The model takes a low-dimensional latent code produced by Gaussian noise as its input. The model comprises two streams. The first stream uses spatiotemporal convolutions to upsample the latent code and generate high-dimensional videos with numerous frames. The second stream controls the background and foreground objects to create the impression of a stationary camera with only the object in motion. The StyleGAN-V is built on the modified StyleGAN2 architecture [15] to synthesise videos [16]. This model utilises a similar concept as the MoCoGAN [13], where the noise vector is split into two vectors--the content vector and the motion vector. However,the motion code in StyleGAN-V is continuous, and this enables the production of very long videos with consistent frames. Unlike previous approaches, where the features of the image and video are evaluated separately by multiple discriminators [13; 17; 18], StyleGAN-V employs a single discriminator that is conditioned on the time distances between the frames. This discriminator is more efficient than traditional video discriminators and provides more effective feedback to the generator. The experimental results have shown that the StyleGAN-V model can produce videos for as long as one hour without any issues, which was a significant challenge for its competitors. There are video GANs that use conditioning data to produce desired videos. For instance, StoryGAN uses a paragraph of text to visualise a story by generating a sequence of images that complement the text [17]. The generator does this in two steps. First, a story encoder maps the text into a low-dimensional vector using a multi-layer perceptron (MLP). Second, a context encoder, which is a recurrent neural network (RNN), tracks the story flow and generates the next image to keep the story moving forward. There are two discriminators--an image discriminator and a story discriminator--that evaluate the genuineness of the story and image. The image discriminator measures whether the generated image matches the sentence, while the story discriminator ensures that the generated image sequence aligns with the story. Mocycle-GAN can translate videos from one form to another; for example, converting videos into segmentation labels and vice versa [19]. It can even transform day-time videos into sunset or night-time environments. Mocycle-GAN maintains consistency in the video by using optical flow and temporal constraints. The model ensures that the video is smooth by maintaining a similar optical flow between adjacent frames throughout the video.