AI RESEARCH
Motion-Aware Caching for Efficient Autoregressive Video Generation
arXiv CS.CV
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ArXi:2605.01725v1 Announce Type: new Autoregressive video generation paradigms offer theoretical promise for long video synthesis, yet their practical deployment is hindered by the computational burden of sequential iterative denoising. While cache reuse strategies can accelerate generation by skipping redundant denoising steps, existing methods rely on coarse-grained chunk-level skipping that fails to capture fine-grained pixel dynamics.