发表机构
University of Science, VNU-HCM; Vietnam National University; University of Dayton（越南胡志明市国家大学; 越南国家大学; Dayton 大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究自回归视频生成中因重复使用潜在变量致时间误差问题，提出SAGA方法，它整合加速域频谱引导目标与初始化策略，无需训练可直接用于现有模型，能提升时间质量、减少时间不稳定性并保持视觉保真度。
AI 中文摘要
自回归视频扩散实现了高效流和长时视频生成，但重复使用生成的潜在变量作为因果上下文会放大时间误差，导致闪烁、运动抖动和结构漂移。本文从频谱运动学角度研究此故障模式，将离散潜在加速度识别为揭示不稳定高频时间扰动的有效信号。为此提出SAGA，一种用于自回归视频生成的无需训练的稳定加速引导方法。它整合了基于有限窗口斯莱皮恩投影的加速域频谱引导目标和结构化自回归噪声初始化策略。实验表明SAGA能提高多个自回归扩散模型的时间质量，减少时间不稳定性并保持视觉保真度。
英文摘要
Autoregressive video diffusion enables efficient streaming and long-horizon video generation, but repeatedly reusing generated latents as causal context can amplify temporal errors, resulting in flickering, motion jitter, and structural drift. In this paper, we investigate this failure mode from a spectral kinematic perspective and identify discrete latent acceleration as an effective signal for revealing unstable high-frequency temporal perturbations. To this end, we propose SAGA, a training-free \textbf{\textit{s}}table \textbf{\textit{a}}cceleration \textbf{\textit{g}}uidance approach for \textbf{\textit{a}}utoregressive video generation. SAGA integrates an acceleration domain spectral guidance objective based on finite-window Slepian projections with a structured autoregressive noise initialization strategy that suppresses short-range temporal correlations while preserving long-range motion structure. Without retraining or modifying the backbone, SAGA can be directly applied to existing chunk-wise autoregressive diffusion models, which is the prevalent setting for high-quality generation. Extensive experiments show that SAGA consistently improves temporal quality across multiple autoregressive diffusion models. On Self-Forcing, SAGA improves Temporal Quality from 97.30 to 97.91 and Image Quality from 69.60 to 70.51. Moreover, spectral analysis and human preference studies demonstrate that SAGA reduces temporal instability while maintaining visual fidelity.
CommentsAccepted to ACCV 2026
