REPARO: LOSS-RESILIENT GENERATIVE CODEC FOR VIDEO CONFERENCING#

Tianhong Li, Vibhaalakshmi Sivaraman, Pantea Karimi, Lijie Fan, Mohammad Alizadeh, Dina Katabi (affiliations not listed in the program)

• Why: A genuinely surprising inversion of loss recovery: instead of adding redundancy (FEC) or retransmitting (impractical for real-time), generate the lost visual content conditioned on what did arrive. Whatever you think of the ethics of hallucinated pixels, it is a new point in the design space for real-time loss resilience.
• What: A video-conferencing framework where a generative model fills in missing frames or frame regions when packets are lost, conditioned on the data received so far and on learned priors of how people and objects appear and move. On public video-conferencing datasets it beats state-of-the-art FEC-based solutions on video quality (PSNR, SSIM, LPIPS) and freeze occurrence (from abstract only).
• How: Conditional generation over partially-received frames, exploiting the model's visual-world priors instead of parity redundancy.
• Where applicable: Real-time interactive video over lossy Internet paths. Research-stage, evaluated on public datasets rather than live conferencing at scale — but as a signal of where loss recovery could go beyond ARQ/FEC, it is worth ten minutes.