Single to Diverse Video Generation Using Conditional Diffusion Probabilistic Model

In the interdisciplinary field of computer vision and deep learning, the generation of video sequences stands as a critical task, with wide-ranging applications from entertainment to surveillance systems. We introduce a novel framework based on the Conditional Diffusion Probabilistic Model (CDPM), featuring significant modifications to the convolution block for handling complex patterns and temporal dynamics. Our model integrates Global Response Normalization (GRN) and Sigmoid Linear Unit (SiLU) activation functions, enhancing its ability to interpret high-dimensional video data. This integration reflects our commitment to advancing video generation technology. The model's effectiveness is validated by its performance on various datasets: achieving an NNFDIV score of 0.57 in the SinGAN-GIF dataset and 0.44 in the HP-VAE-GAN dataset, outperforming existing methods. It also excels in NIQE, RECO, and PSNR metrics, indicative of superior image quality and coherence. These results collectively highlight the model's robustness in diverse video generation, marking a notable advancement in computer vision and deep learning.