用生成模型直接解码，零样本实现超低码率视频压缩
GVCC: Zero-Shot Video Compression via Codebook-Driven Stochastic Rectified Flow
- 将确定性生成流程转为带随机性的信息通道，可编码每步创新
- 在0.003 bpp下LPIPS最低，相比基线降低65%
- 支持文本/图像/首尾帧生成，适合无参考视频压缩场景
在极低码率下，高保真重建需从后验分布采样真实视频，而非回归到过度平滑的条件均值。我们提出生成式视频码本编码器（GVCC），一种零样本框架：预训练视频生成模型直接作为解码器，传输比特流指定其生成轨迹。现代修正流视频模型通常使用确定性微分方程求解器采样，无法提供逐步随机通道以传输压缩信息。GVCC通过将确定性流采样器转换为等价的边缘保持随机过程，使信息可通过编码每步随机创新来传递。与图像不同，视频具有更长的时间依赖性和更多样的条件模式。我们在三种实用模式中实现了GVCC：无参考的文本到视频（T2V）、带尾部潜在修正的自回归图像到视频（I2V），以及采用边界共享图像组（GOP）链式的首尾帧到视频（FLF2V）。在UVG数据集上，GVCC在三个代表性码率区间内达到最低的LPIPS，最低达约0.003 bpp，相较于同码率下的DCVC-RT，LPIPS降低65%。
原文摘要 · Abstract (English)
At ultra-low bitrates, high-fidelity reconstruction requires sampling plausible videos from the posterior rather than regressing to oversmoothed conditional means. We propose Generative Video Codebook Codec (GVCC), a zero-shot framework in which a pretrained video generative model serves directly as the decoder, and the transmitted bitstream specifies its generation trajectory. Modern rectified-flow video models are typically sampled with deterministic ODE solvers, which leave no per-step stochastic channel for transmitting compressed information. GVCC addresses this by converting the deterministic flow sampler into an equivalent marginal-preserving stochastic process, so that information can be transmitted by encoding the per-step stochastic innovations. Unlike images, videos introduce longer temporal dependencies and more diverse conditioning modes. We instantiate GVCC in three practical modes: Text-to-Video (T2V) without a reference frame, autoregressive Image-to-Video (I2V) with tail latent correction, and First-Last-Frame-to-Video (FLF2V) with boundary-sharing Group of Pictures (GOP) chaining. On UVG, GVCC achieves the lowest LPIPS among evaluated baselines across three representative bitrate regimes (down to ${\sim}$0.003\,bpp), with 65\% LPIPS reduction over DCVC-RT at matched bitrate.
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