结合自回归与双向视频扩散的统一生成式视频压缩
Unified Generative Video Compression with Autoregressive and Bidirectional Video Diffusion
AI总结：
提出统一生成式视频编解码器UniGVC，支持双向模式的高质量解码与自回归模式的低延迟流式传输，在1080p基准测试中实现了优异的压缩性能与解码效率。
摘要：
视频扩散模型可在超低码率下从严重压缩的表示中重建视觉效果出色的内容，但它们存在严格的部署权衡问题。双向（BI）模型通过密集的全序列注意力实现高质量，但需要完整序列才能重建，且会产生显著延迟；自回归（AR）模型支持低延迟流式传输，但其受限的时间上下文会降低压缩效率。这种权衡在视频流必须实时解码且同时存储以供后续高质量回放时尤为突出，因此AR和BI模型分别成为直播和回放阶段的自然选择。为支持这两个阶段，我们提出UniGVC，一种统一生成式视频编解码器，可从单一比特流支持两种解码模式。UniGVC-BI采用带有密集注意力的BI-DiT，用于高质量随机访问解码；UniGVC-AR则使用窗口化AR-DiT，用于低延迟流式传输。两条路径共享一个分层潜在编解码器，该编解码器以超低码率循环生成相同条件，使UniGVC-BI可作为条件教师，用于UniGVC-AR的单步蒸馏。一个小型条件视频解码器进一步降低了流式延迟，同时保留了大部分重建质量。在标准1080p基准测试中，UniGVC-BI在评估方法中实现了最佳的率-失真-感知权衡，且在低至0.0005比特每像素的码率下生成时间稳定的视频；同时，UniGVC-AR在以36.85 FPS解码1080p视频时仍保持竞争力，速度比UniGVC-BI快250倍以上。代码和模型将后续发布。
英文摘要：
Video diffusion models can reconstruct visually compelling content from severely compressed representations at ultra-low bitrates, but they impose a rigid deployment trade-off. Bidirectional (BI) models achieve high quality through dense full-sequence attention, yet require the entire sequence before reconstruction and incur substantial latency. Autoregressive (AR) models enable low-delay streaming, but their restricted temporal context reduces compression efficiency. This tension is particularly relevant when a video stream must be decoded in real time yet is also stored for later, quality-oriented playback, making AR and BI models natural choices for the live and replay stages, respectively. To support both stages, we present UniGVC, a Unified Generative Video Codec that supports two decoding modes from a single bitstream. UniGVC-BI employs a BI-DiT with dense attention for quality-oriented random-access decoding, whereas UniGVC-AR uses windowed AR-DiT for low-delay streaming. Both paths share a hierarchical latent codec that recurrently produces the same conditions with ultra-low bitrates, allowing UniGVC-BI to serve as a conditional teacher for single-step distillation of UniGVC-AR. A tiny conditional video decoder further reduces streaming latency while preserving most of the reconstruction quality. On standard 1080p benchmarks, UniGVC-BI achieves the best overall rate-distortion-perception trade-off among the evaluated methods and produces temporally stable video at rates as low as 0.0005 bits per pixel. Meanwhile, UniGVC-AR remains competitive while decoding 1080p video at 36.85 FPS, over $250\times$ faster than UniGVC-BI. Code and models will be released later.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
