# Dynamic Fashion Video Synthesis from Static Imagery
## 6 Conclusions  
Our research introduces a novel architecture for diffusion models that is tailored specifically for synthesising high-fidelity fashion videos using conditional images. We propose a methodology that utilises pseudo-3D convolution, VAE, and CLIP encoder to condition synthesised videos both on a global and local scale. Our approach represents a significant advancement over previous efforts in this domain, such as synthesising videos at a faster pace and not requiring person-specific fine-tuning. Additionally, it produces videos that are temporally coherent and capture vital details from the conditioning image. We conducted a thorough comparison of our image-to-video model with various other models. We demonstrated that the video quality produced by our model is significantly better than the GAN-based methods. Our model generates videos with a higher resolution, allowing for a more detailed output. Additionally, we observed that our model produces videos that have better temporal consistency, meaning that the frames flow more seamlessly from one to the other.  
Our diffusion model is unique compared to other diffusion models. Most of the previous works are text-to-video or image-to-video with pre-recorded pose sequences like DreamPose. Unlike these models, our diffusion model generates spontaneous movements that are realistic and distinctive without the need for pre-recorded data. This makes our model more practical and efficient, as it only requires one image as input.  
We conducted an ablation study to evaluate the effectiveness of our approach. Our results show that conditioning the latent diffusion U-Net on local and global scales allows the model to preserve the most detail from the condition image. By doing so, our method can generate high-quality videos that demonstrate a high degree of fidelity and realism.  
Overall, our work highlights the potential of combining deep learning techniques to synthesise high-quality videos with greater efficiency and precision. Our approach has significant implications for the fashion industry, where the ability to create high-quality videos is becoming increasingly important for marketing and showcasing products in a competitive setting.  
The proposed image-to-video synthesis model also has a wide range of potential applications. At this stage, our model can be valuable for generating preliminary visualisations, concept art, and storyboarding elements for filmmakers and content creators, which can then be manually refined for final production. Video game developers might use it for early-stage animations, cutscenes, or prototyping visual elements before detailed finalisation. Additionally, artists and designers can leverage the model for experimental visual projects and initial drafts of visual artworks or animations that do not require extreme detail. In the future, when this model is improved, it can be used for higher-end applications.  
Conceptualisation, T.I., A.M. and Y.L.; methodology, T.I.; software, T.I.; validation, T.I.; formal analysis, T.I., A.M. and Y.L.; writing--original draft preparation, T.I; writing--review and editing, T.I., A.M., X.L. and Y.L.; visualisation, T.I.; supervision, A.M. and Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Engineering and Physical Sciences Research Council (EPSRC) grant number EP/T518116/1.  
This paper did not generate any new data.  
The authors declare no conflicts of interest.