[2512.00408] Low-Bitrate Video Compression through Semantic-Conditioned Diffusion

Computer Science > Computer Vision and Pattern Recognition arXiv:2512.00408 (cs) [Submitted on 29 Nov 2025 (v1), last revised 6 Apr 2026 (this version, v2)] Title:Low-Bitrate Video Compression through Semantic-Conditioned Diffusion Authors:Lingdong Wang, Guan-Ming Su, Divya Kothandaraman, Tsung-Wei Huang, Mohammad Hajiesmaili, Ramesh K. Sitaraman View a PDF of the paper titled Low-Bitrate Video Compression through Semantic-Conditioned Diffusion, by Lingdong Wang and 5 other authors View PDF HTML (experimental) Abstract:Traditional video codecs optimized for pixel fidelity collapse at ultra-low bitrates and produce severe artifacts. This failure arises from a fundamental misalignment between pixel accuracy and human perception. We propose a semantic video compression framework named DiSCo that transmits only the most meaningful information while relying on generative priors for detail synthesis. The source video is decomposed into three compact modalities: a textual description, a spatiotemporally degraded video, and optional sketches or poses that respectively capture semantic, appearance, and motion cues. A conditional video diffusion model then reconstructs high-quality, temporally coherent videos from these compact representations. Temporal forward filling, token interleaving, and modality-specific codecs are proposed to improve multimodal generation and modality compactness. Experiments show that our method outperforms baseline semantic and traditional codecs by 2-10X ...