Comparative analysis of Vid2Vid and Fast Vid2Vid Models for Video-to-Video Synthesis on Cityscapes Dataset

Video generation refers to the process of creating videos using computational methods with the help of deep learning algorithms. With the advancement of deep learning techniques and the availability of large datasets, video generation has become more sophisticated and has the potential to revolutionize various industries, including entertainment, advertising, and education. This comparative review of video-to-video synthesis models is done to understand the working of both the Vid2Vid and Fast Vid2Vid model. In this comparative review, Vid2Vid delivers 3.44 FID and 0.92 Human Preference Score. In contrast, Fast Vid2Vid, with a two-stage training process, fine-tunes the Vid2Vid model on segmented images and gains 3.10 FID and 0.97 Human Preference Score. Fast Vid2Vid generates high-quality videos at a faster speed than Vid2Vid, making it more suitable for various applications like video editing, video creation, virtual try-on and so on.