Reparo: Loss-Resilient Generative Codec for Video Conferencing

Tianhong Li, Vibhaalakshmi Sivaraman, Lijie Fan, Mohammad Alizadeh, Dina Katabi

Loss of packets in video conferencing often results in poor quality and video freezing. Attempting to retransmit the lost packets is usually not practical due to the requirement for real-time playback. Using Forward Error Correction (FEC) to recover the lost packets is challenging since it is difficult to determine the appropriate level of redundancy. In this paper, we propose a framework called Reparo for creating loss-resilient video conferencing using 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 so far, and the model's knowledge of how people look, dress, and interact in the visual world. Our experiments on publicly available video conferencing datasets show that Reparo outperforms state-of-the-art FEC-based video conferencing in terms of both video quality (measured by PSNR) and video freezes.

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