  Packet loss during video conferencing often leads to poor quality and video
freezing. Attempting to retransmit lost packets is often impractical due to the
need for real-time playback. Employing Forward Error Correction (FEC) for
recovering the lost packets is challenging as it is difficult to determine the
appropriate redundancy level. To address these issues, we introduce Reparo -- a
loss-resilient video conferencing framework based on generative deep learning
models. Our approach involves generating missing information when a frame or
part of a frame is lost. This generation is conditioned on the data received
thus far, taking into account the model's understanding of how people and
objects appear and interact within the visual realm. Experimental results,
using publicly available video conferencing datasets, demonstrate that Reparo
outperforms state-of-the-art FEC-based video conferencing solutions in terms of
both video quality (measured through PSNR, SSIM, and LPIPS) and the occurrence
of video freezes.
