An Analysis of Solutions to the Frame Consistency Problem in Video style Transfer based on GAN

Abstract

Video style transfer has emerged as a significant area within the realm of neural style transfer, holding promise for diverse applications. In this context, GANs have gained traction for their advantages in addressing temporal consistency concerns. This paper explores various strategies to manage frame consistency within GAN-based video-style conversion. It delves into techniques such as Recurrent Neural Networks (RNNs), 3D convolutions, inter-frame continuity in the discriminator, and time loss functions. The research highlights key studies that employ RNNs to complement GAN-based frameworks for improved temporal consistency. The study also evaluates a range of other approaches, such as introducing inter-frame continuity in the discriminator and applying time loss functions to minimize visual discrepancies. Through this analysis, the paper contributes insights into the evolving landscape of video style transfer techniques, guiding researchers towards effective strategies for achieving frame consistency and advancing the field.