发表机构
Nanjing University（南京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有Promptus框架受网络波动影响易出现质量崩溃的问题，提出ScalablePromptus，通过多项改进使其在有损网络下性能下降降低82%-95%，可稳健用于实际部署。
AI 中文摘要
基于提示的视频流传输紧凑的语义提示而非像素级内容以进行生成重建，可实现超低比特率通信。但当前最先进的Promptus框架易受网络波动影响，部分接收的提示会导致严重的质量崩溃。我们提出ScalablePromptus，它通过语义和颜色感知的提示反转、中间帧的球面线性插值，以及最关键的、能生成有序提示表示的丢弃训练策略来增强Promptus，使接收方可从任意截断的提示中重建有意义的视频而无需任何适配。在稳定网络下，ScalablePromptus实现了适度的质量提升；在有损条件下，与基线相比，它将截断导致的性能下降降低了82%-95%，使基于提示的流足够稳健以用于实际部署。
英文摘要
Prompt-based video streaming transmits compact semantic prompts instead of pixel-level content for generative reconstruction, enabling ultra-low-bitrate communication. However, the state-of-the-art Promptus framework is vulnerable to network fluctuation, where partially received prompts lead to catastrophic quality collapse. We propose ScalablePromptus, which enhances Promptus with semantic and color-aware prompt inversion, spherical linear interpolation for intermediate frames, and--most critically--a dropout training strategy that produces rank-ordered prompt representations. This allows the receiver to reconstruct meaningful video from arbitrarily truncated prompts without any adaptation. Under stable networks, ScalablePromptus achieves modest quality gains. Under lossy conditions, it reduces the performance degradation caused by truncation by 82%-95% compared to the baseline, making prompt-based streaming robust enough for real-world deployment.
Comments10 pages, 8 figures
