Motion-Aware Caching for Efficient Autoregressive Video Generation
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| Authors | Jing Xu et al. |
| Year | 2026 |
| HF Upvotes | 6 |
| arXiv | 2605.01725 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
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. This oversight is critical: pixels with high motion require more denoising steps to prevent error accumulation, while static pixels tolerate aggressive skipping. We formalize this insight theoretically by linking cache errors to residual instability, and propose MotionCache, a motion-aware cache framework that exploits inter-frame differences as a lightweight proxy for pixel-level motion characteristics. MotionCache employs a coarse-to-fine strategy: an initial warm-up phase establishes semantic coherence, followed by motion-weighted cache reuse that dynamically adjusts update frequencies per token. Extensive experiments on state-of-the-art models like SkyReels-V2 and MAGI-1 demonstrate that MotionCache achieves significant speedups of 6.28times and 1.64times respectively, while effectively preserving generation quality (VBench: 1%downarrow and 0.01%downarrow respectively). The code is available at https://github.com/ywlq/MotionCache.
Engineering Breakdown
Plain English
This paper addresses a real bottleneck in autoregressive video generation: existing cache-reuse strategies skip denoising steps uniformly across all pixels, but pixels with high motion actually need more refinement to avoid error accumulation. The authors propose MotionCache, which uses inter-frame differences to identify which pixels can safely skip denoising steps and which need full processing, achieving faster generation without quality loss.
Key Engineering Insight
Motion-aware selective denoising is orders of magnitude more efficient than uniform cache skipping because static regions can be aggressively cached while high-motion regions get the compute they need. This is formalized through the link between cache reuse errors and residual instability — a principle that applies beyond just video generation.
Why It Matters for Engineers
Video generation inference is prohibitively slow for real products. If you're building a video generation service, uniform caching leaves huge efficiency gains on the table. Motion-aware caching lets you generate longer sequences or serve more concurrent users with the same hardware, which directly impacts cost-per-inference and product feasibility.
Research Context
Prior work on autoregressive video generation uses coarse-grained chunk-level cache skipping, which treats all pixels equally. This paper advances the field by recognizing that cache strategies should be pixel-spatially adaptive. It enables practical long-video synthesis by bridging the gap between theoretical promise of autoregressive models and their actual deployment constraints.
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