# Dynamic Fashion Video Synthesis from Static Imagery
## 2 Background
### Diffusion Model  
GANs and diffusion models are two different techniques used to generate data. While GANs use adversarial training to improve the generator, diffusion models gradually add noise to real data to create a series of distorted versions. The model then learns to reverse this process by removing the noise sequentially to generate a coherent image or other data types, starting from random noise. Similar to GANs, diffusion models offer the capability to manipulate the generated images using textual descriptions [6] or input images [25]. This adaptability opens up a wide array of possibilities for their use in the fashion industry. Diffusion models have emerged as a promising approach to generating videos. Numerous research studies have investigated the potential use of these models in this domain. Recent works such as [26; 27; 28; 29; 30; 31; 32; 33] have shown that diffusion models are currently the most active area of research in the development of generative models for videos. Notably, these models have demonstrated their ability to generate videos with the best quality and temporal consistency. Many researchers have pointed out that training diffusion models with large-scale video datasets are computationally expensive [26; 27; 28; 34]. Some techniques have tackled this problem by leveraging models that are trained on datasets consisting of pairs of text and images [26; 27]. These models are designed to understand how the world looks based on textual input, and they are then trained on unsupervised video footage to learn how images move in the real world [26]. In the field of video generation using diffusion models, most models rely on a text-to-video approach, where textual inputs are used to create videos [26; 27; 33; 35; 36]. Only a few models have demonstrated synthesis videos from conditioning images [26; 37; 38]. The video diffusion model [39] is the first approach to use the diffusion model for generating unconditional videos. This model generates low-resolution video frames first and then leverages a super-resolution module to upsample them. The key feature of this model is that it is trained to approximate the complex distributions of raw videos, which is computationally challenging. Make-A-Video [26] utilises existing text-to-image models and converts them into text-to-video models through the use of spatiotemporal attention and convolution. These techniques enable the model to preserve the video's quality and ensure it has temporal smoothness. Similar to ImaGINator [18], Make-A-Video uses pseudo-3D convolutional layers where the 2D convolution is stacked against a 1D convolution. They would use unsupervised learning on unlabeled video data to teach a model how an image would move. The decoder generates low-resolution, low-framerate video frames, which are then interpolated to a higher frame rate and resolution as data passes through the decoder layers. MagicVideo is an efficient model for text-to-video synthesis [35]. It uses a latent diffusion model (LDM) [6], which is an efficient approach to denoise the latent space to a lower dimension, making the entire process faster. Compared to other video synthesis tools, such as Make-A-Video [26], MagicVideo employs a 2D convolution with temporal computation operators to model both the spatial and temporal features of the video. The temporal computation operator is a lightweight adapter that can effectively exploit the correlation between video frames. Tune-a-video trains a text-to-video generator using a single text-video pair and a pre-trained text-to-image model [27]. The spatiotemporal attention queries relevant positions in previous frames to ensure consistency with the generated video's temporal dimension. During the inference stage, structure guidance from the source video is incorporated. This means that the latent noise of the source video is obtained from the diffusion model with no textual condition.