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RTFVE: Realtime Face Video Enhancement

arXiv 2607.11034 ↗First posted 13 Jul 2026cs.CVcited by 0

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Abstract (arXiv)

There's been a surge in adoption of video conferencing applications for both personal and business use cases. However, the bandwidth limitations faced by many users worldwide may restrict the optimal use of such applications. Although deep learning offers a solution for enhancing low bit rate videos, most models today are either hard to incorporate with modern compression standards or require specialized hardware to run such as significant GPUs making these models impractical. To address these issues, we introduce the Realtime Face Video Enhancement (RTFVE) model which can be easily incorporated with any video decoder and can run in realtime on ordinary CPUs. Experiments show that our model improves perceptual quality over the compressed video baseline at multiple low bitrate settings. The source code will be made available at https://github.com/varun-jois/RTFVE.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 23 references that are in the map. First posted on arXiv 13 Jul 2026.

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References · 16

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4 more
  1. 2021Robust Reference-based Super-Resolution via C2-Matchingcited by 68
  2. 2019MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Videocited by 50
  3. 2023PGDiff: Guiding Diffusion Models for Versatile Face Restoration via Partial Guidancecited by 49
  4. 2023Perceptual Quality Improvement in Videoconferencing using Keyframes-based GANcited by 6

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