# Dynamic Fashion Video Synthesis from Static Imagery
## 4 Experiments
### Quantitative Analysis  
Video synthesis has received relatively less attention compared to image synthesis, resulting in a limited number of evaluation metrics available to assess the quality of synthesised videos. In this context, we have utilised evaluation metrics commonly employed for assessing the quality of fashion videos in the realm of video virtual try-on [42]. These metrics have also been adopted by other researchers working in the field of video synthesis, such as Poke [24] and cINNs [23]. Furthermore, the algorithms do not factor in the playback speed. Irrespective of whether the video is played at 30 or 60 frames per second, these algorithms assess each video frame independently and its complementarity with the others. Frechet video distance (FVD) is an effective metric for evaluating the quality of a synthesised video [69; 70]. It is influenced by the frechet inception distance (FID) [71], which is used for evaluating synthesised images. FVD introduces a feature representation that captures the temporal coherence of the content of a video, in addition to the quality of each frame. FVD consistently outperforms structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) in terms of agreeing with human judgment [69]. FVD evaluates 16 frames of a video using the pre-trained inflated 3D convnet model (I3D) [72], which was designed to recognise the act performed in a video. The number of frames cannot be altered according to the implementation of [73]. However, the VFID I3D implementation by [74] is identical to FVD, except their I3D model can evaluate up to 60 frames. The equation for FVD and VFID I3D is denoted as follows:  
\[d(P_{R},P_{G})=|\mu_{R}+\mu_{G}|^{2}+\text{Tr}(\Sigma_{R}+\Sigma_{G}-2(\Sigma_{ R}\Sigma_{G})^{1/2}) \tag{4}\]  
where \(R\) represents the video from the dataset, \(G\) is the generated video, the variables \(\mu_{R}\) and \(\mu_{G}\) represent the mean, while \(\Sigma_{R}\) and \(\Sigma_{G}\) represent the covariance matrices of the recorded activation data from both the real \(P_{R}\) and generated \(P_{G}\) videos, respectively. \(P_{R}\) and \(P_{G}\) were obtained by passing the videos through a pre-trained I3D, which takes into account the visual content's temporal coherence across a sequence of frames. Figure 3: Qualitative comparison of our method against ImaGINator [18], cINNs [23], Poke [24], and DreamPose [38]. Subfigures (**a–c**) show how our model and competitors handle conditional images with different coloured dresses and people. Our method executes a wider range of movements, showcasing the clothing from various angles. Our result is highly comparable to that of DreamPose in terms of both quality and temporal consistency. This means that our method is capable of producing high-quality results consistently over time, providing users with a reliable and efficient solution for their needs. The inception score (IS) serves as a tool to assess the performance of generative models [75]. Its purpose is to gauge both the variety and the aesthetic appeal of the images produced by these models. It achieves this by running the generated images through a classifier that has been trained beforehand and then determines a score based on the resulting probabilities. Specifically, the IS is derived from the exponential of the average KL divergence between the class distribution of the generated images and the class distribution of a large collection of real images. A higher IS suggests that the generated images possess greater diversity and visual appeal. \[\text{IS}=\exp\bigl{(}\mathbb{E}_{x\sim P_{G}}[D_{\text{KL}}(P(y|x)\|P(y))] \bigr{)} \tag{5}\]  
where \(P_{G}\) represents the distribution of the generated images, the notation \(\mathbb{E}_{x\sim P_{G}}\) signifies that we are taking the average over samples \(x\) drawn from this distribution. The conditional distribution \(P(y|x)\) indicates how labels \(y\) (values obtained from a pre-trained classifier) are distributed when we have a generated image \(x\).