A Generative Face Video Coding Framework with Disentangled and Consistent Background
Shiv Gehlot, Guan‐Ming Su, Peng Yin, Sean T. McCarthy, Gary J. Sullivan · 2025
Existing Generative face video coding (GFVC) frameworks enable the ultra-low bandwidth video communication through transmission of compact facial representations. However, non-localization of derived facial representations leads to foreground and background blending (entanglement) in the decoded sequences, resulting in geometry distortions. In this work, we propose a GFVC framework that removes this blending, and suppresses the induced artifacts. To achieve the disentanglement, the proposed framework 1) separately transmits foreground and background of the first frame (base pictures), and 2) performs fusion at the decoder end with background base picture. Further, the proposed methodology supports chroma keying for decoder simplification and background customization. The proposed approach is generic and builds upon existing GFVC frameworks to generate stable and consistent video sequences. Compared to VVC, proposed algorithm reduces the average bit rate by 52.13% and offers a 1.24% average improvement in DISTS over existing GFVC methods at QP 22. Additionally, subjective evaluations reveal a 88.89% preference for the proposed approach, in contrast to 11.11% for current best-performing method.