CVDec 4, 2025

Generative Neural Video Compression via Video Diffusion Prior

arXiv:2512.05016v24 citationsh-index: 4
Originality Incremental advance
AI Analysis

This work addresses flickering artifacts in perceptual video compression for applications requiring high-quality video at low bitrates, representing an incremental advance by integrating video-native generative priors.

The paper tackled the problem of perceptual flickering in neural video compression by introducing GNVC-VD, a framework that uses a video diffusion transformer for sequence-level refinement, resulting in improved perceptual quality and reduced artifacts even at bitrates below 0.01 bpp.

We present GNVC-VD, the first DiT-based generative neural video compression framework built upon an advanced video generation foundation model, where spatio-temporal latent compression and sequence-level generative refinement are unified within a single codec. Existing perceptual codecs primarily rely on pre-trained image generative priors to restore high-frequency details, but their frame-wise nature lacks temporal modeling and inevitably leads to perceptual flickering. To address this, GNVC-VD introduces a unified flow-matching latent refinement module that leverages a video diffusion transformer to jointly enhance intra- and inter-frame latents through sequence-level denoising, ensuring consistent spatio-temporal details. Instead of denoising from pure Gaussian noise as in video generation, GNVC-VD initializes refinement from decoded spatio-temporal latents and learns a correction term that adapts the diffusion prior to compression-induced degradation. A conditioning adaptor further injects compression-aware cues into intermediate DiT layers, enabling effective artifact removal while maintaining temporal coherence under extreme bitrate constraints. Extensive experiments show that GNVC-VD surpasses both traditional and learned codecs in perceptual quality and significantly reduces the flickering artifacts that persist in prior generative approaches, even below 0.01 bpp, highlighting the promise of integrating video-native generative priors into neural codecs for next-generation perceptual video compression.

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