# Dynamic Fashion Video Synthesis from Static Imagery
## 2 Background
### Deep Learning for Fashion Application  
There are several deep learning models that can be used to support the fashion industry, as mentioned in [2]. For instance, virtual try-ons [3; 4; 40; 41; 42; 43] give customers the ability to merge images of clothing items with their own images, allowing them to see how the garment will look and fit on them realistically. Facial makeup transfer allows the model to transfer makeup from a reference image to a source image [44]. Additionally, pose transfer can show different viewing angles of fashion products by altering the posture of a person in the image [45; 46]. Image-based fashion recommendation systems (FRSs) offer consumers a highly personalised shopping experience by leveraging their browsing history and previous purchase records to provide tailored recommendations. Among these FRSs, some employ deep learning techniques [47; 48]. These advanced systems go beyond basic recommendations by predicting compatibility scores between clothing items, particularly in terms of their style, such as colours and patterns. For instance, when a customer is interested in purchasing a t-shirt, these models can thoroughly analyse the chosen t-shirt and, based on its style attributes, suggest the most suitable trousers that complement the selected t-shirt. Essentially, they make informed decisions on behalf of the customer, ensuring that the clothing combinations harmonise seamlessly. This functionality greatly improves the shopping experience for customers, making it not only convenient but also enhancing their overall satisfaction with the process [49]. Video virtual try-on, as demonstrated in works like [50; 51; 52; 53], take virtual try-on experiences to the next level by seamlessly integrating clothing onto a person in a video. This dynamic approach enables customers to witness clothing items in motion as they respond and adapt to the wearer's movements. Notably, these models capture the subtle nuances of how clothing products move and flow as the person walks, gestures, or performs various activities. This level of detail and realism is exceptionally informative, as it allows customers to gauge not just how a garment looks when stationary but also how it behaves during real-world activities. Consequently, a virtual video try-on elevates the online shopping experience by providing a holistic view of a clothing item's fit, comfort, and aesthetic appeal in motion. Although the use of diffusion models in fashion-related tasks is a recent development, only a few studies have explored this approach thus far [38; 54; 55]. Some of these studies include DreamPose [38], a fashion video synthesis model that uses conditional poses to guide video synthesis, and DiffFashion [54], a model that combines texture from one image with a fashion product. These models demonstrate the applicability of diffusion models in the fashion industry and their potential to revolutionise various aspects of it, from creative content generation and virtual try-ons to personalised styling recommendations and trend forecasting. Our work solves a similar problem presented in DreamPose [38], where we generate videos from a single image. However, we differ from DreamPose in terms of producing spontaneous yet believable motions, while DreamPose uses pre-recorded posture data to guide video synthesis. Additionally, we utilise cross-attention [9] mechanisms that simultaneously condition all video frames. In contrast, DreamPose synthesises each video frame separately and applies cross-attention individually, making their model much slower than ours. Compared to previous work, our goal is to make generative AI more attractive to businesses by developing more computationally efficient models. This means that businesses will not need to invest in expensive AI accelerators. Furthermore, efficient models can generate videos more quickly, thus preventing the testing of their customers' patience.