Reconstructing the Invisible: Video Frame Restoration through Siamese
  Masked Conditional Variational Autoencoder
Yongchen Zhou Richard Jiang
In the domain of computer vision, the restoration of missing information in
video frames is a critical challenge, particularly in applications such as
autonomous driving and surveillance systems. This paper introduces the Siamese
Masked Conditional Variational Autoencoder (SiamMCVAE), leveraging a siamese
architecture with twin encoders based on vision transformers. This innovative
design enhances the model's ability to comprehend lost content by capturing
intrinsic similarities between paired frames. SiamMCVAE proficiently
reconstructs missing elements in masked frames, effectively addressing issues
arising from camera malfunctions through variational inferences. Experimental
results robustly demonstrate the model's effectiveness in restoring missing
information, thus enhancing the resilience of computer vision systems. The
incorporation of Siamese Vision Transformer (SiamViT) encoders in SiamMCVAE
exemplifies promising potential for addressing real-world challenges in
computer vision, reinforcing the adaptability of autonomous systems in dynamic
environments.