Building on recent advances in video generation, generative video compression has emerged as a new paradigm for achieving visually pleasing reconstructions. However, existing methods exhibit limited exploitation of temporal correlations, causing noticeable flicker and degraded temporal coherence at ultra-low bitrates. In this paper, we propose Free-GVC, a training-free generative video compression framework that reformulates video coding as latent trajectory compression guided by a video diffusion prior. Our method operates at the group-of-pictures (GOP) level, encoding video segments into a compact latent space and progressively compressing them along the diffusion trajectory. To ensure perceptually consistent reconstruction across GOPs, we introduce an Adaptive Quality Control module that dynamically constructs an online rate-perception surrogate model to predict the optimal diffusion step for each GOP. In addition, an Inter-GOP Alignment module establishes frame overlap and performs latent fusion between adjacent groups, thereby mitigating flicker and enhancing temporal coherence. Experiments show that Free-GVC achieves an average of 93.29% BD-Rate reduction in DISTS over the latest neural codec DCVC-RT, and a user study further confirms its superior perceptual quality and temporal coherence at ultra-low bitrates.

翻译：基于视频生成领域的最新进展，生成式视频压缩已成为实现视觉愉悦重建的新范式。然而，现有方法对时序相关性的利用有限，导致在超低码率下出现明显的闪烁现象和时序一致性退化。本文提出Free-GVC，一种无需训练的生成式视频压缩框架，该框架将视频编码重新定义为由视频扩散先验引导的潜在轨迹压缩。我们的方法在图像组（GOP）级别运行，将视频片段编码到紧凑的潜在空间中，并沿着扩散轨迹逐步压缩它们。为确保跨GOP的感知一致性重建，我们引入了一个自适应质量控制模块，该模块动态构建在线码率-感知代理模型，以预测每个GOP的最佳扩散步数。此外，一个GOP间对齐模块建立了相邻组之间的帧重叠并执行潜在融合，从而减轻闪烁并增强时序一致性。实验表明，Free-GVC在DISTS指标上相比最新的神经编解码器DCVC-RT平均实现了93.29%的BD-Rate节省，用户研究进一步证实了其在超低码率下卓越的感知质量和时序一致性。