StyleGAN based path analysis from image to video generation in multimedia generation

The recent speed with which Artificial Intelligence (AI) and Deep learning (DL) are being evolved, has dramatically changed the manner in which multimedia content is generated especially in the processing of static multimedia images to dynamic video sequences. The ability to create dynamic visual content or visual content dynamically using a single static image is one of these developments, and is investigated as a compelling field of research with a broad spectrum of applications in entertainment, film making, virtual reality, digital avatars, and medical imaging. The paper is a thorough Path Analysis of Multimedia Generation (PAMG) system with Generative Adversarial Networks (GANs) in Image-to-Video Generation. The study centers on a principle of Latent Flow Diffusion Model (LFDM) so as to reach the high-fidelity of spatial and temporal consistency within the generated videos. The data of the images were obtained through publicly available datasets of varying human activities and facial expression. The preprocessing included the resizing of the images and as well as normalization of the data. A Convolutional Neural Network (CNN) was also used to extract latent spatial content and motion representations that were used to extract features. A Style-based Generative Adversarial Network (StyleGAN) was used to generate realistic video frames conditioned on the latent representations. The proposed framework operates in two main stages. First, an unsupervised latent flow auto-encoder (LFAE) is used to learn spatial content and inter-frame motion. Next, a 3D-ResNet-based DM generates temporally coherent latent flow sequences based on the input image and action label. These flows are used to warp the input image into a complete video sequence. Performance evaluation of the suggested method is effective for archive PSNR (33.62), LPIPS (0.045), FID (24.87), FVD (226.35), Prompt Consistency (39.42), Frame Consistency (0.9932), and Average Displacement (14.75). Additionally statistical hypothesis testing was performed using paired t-tests and one-way ANOVA, confirming that these improvements are statistically significant (p < 0.001). Experimental results demonstrate that the proposed GAN-based multimedia generation framework significantly outperforms baseline methods in terms of visual realism and motion coherence, representing a notable advancement in image-to-video synthesis.