Motion-Aware Caching for Efficient Autoregressive Video Generation

Official implementation of MotionCache

Paper | Code

MotionCache is a training-free, motion-aware caching framework for efficient autoregressive video generation.

Overview
Method
Main Results
Quick Start
Citation
Acknowledgments

Overview

Autoregressive video generation can synthesize long videos by generating chunks sequentially, but each chunk still requires expensive iterative denoising. Existing cache-reuse methods usually make coarse chunk-level reuse decisions, which can miss fine-grained motion differences inside a video: highly dynamic tokens need more updates, while static tokens can be reused more aggressively.

MotionCache addresses this with motion-aware token-level caching. It uses inter-frame differences as a lightweight proxy for motion importance, then applies motion-weighted reuse so different tokens can receive different update frequencies during generation.

Method

MotionCache follows a coarse-to-fine caching strategy:

Warm-up for semantic coherence: early denoising steps are computed normally to establish stable video content.
Motion-aware token weighting: inter-frame differences estimate which spatial tokens are more motion-sensitive.
Token-wise cache reuse: static or low-motion regions reuse cached activations more aggressively, while high-motion regions are recomputed more often to reduce error accumulation.
Model-agnostic integration: the method is implemented on both MAGI-1 and SkyReels-V2 without retraining.

For full derivations and ablations, please refer to the paper.

Main Results

MotionCache improves the speed-quality tradeoff on both SkyReels-V2 and MAGI-1. The table below reports the main VBench results from the paper.

SkyReels-V2

Method	PFLOPs	Speedup	Latency (s)	VBench	PSNR	SSIM	LPIPS
Vanilla	113	1.00x	1540	83.84%	-	-	-
TeaCache-slow	58	1.89x	814	82.67%	21.96	0.7501	0.1472
TeaCache-fast	49	2.20x	686	80.06%	18.39	0.6121	0.3063
FlowCache-slow	31	6.26x	246	82.70%	21.83	0.8733	0.1417
FlowCache-fast	27	7.19x	214	82.38%	21.17	0.8697	0.1634
MotionCache-slow	30	6.28x	245	82.84%	23.46	0.9093	0.0875
MotionCache-fast	26	7.26x	212	82.75%	21.78	0.8723	0.1478

MAGI-1

Method	PFLOPs	Speedup	Latency (s)	VBench	PSNR	SSIM	LPIPS
Vanilla	139	1.00x	1520	77.26%	-	-	-
TeaCache-slow	129	1.14x	1339	76.64%	14.74	0.4132	0.6189
TeaCache-fast	101	1.41x	1075	68.81%	11.98	0.2632	0.7670
FlowCache-slow	104	1.39x	1094	77.08%	18.16	0.6486	0.3451
FlowCache-fast	78	1.94x	782	73.42%	14.92	0.3998	0.6088
MotionCache-slow	100	1.64x	925	77.25%	19.71	0.7231	0.2510
MotionCache-fast	64	2.07x	733	74.59%	17.70	0.5600	0.4861

Quick Start

Environment preparation follows the corresponding base projects and the FlowCache setup style. After dependencies and model checkpoints are ready, run MotionCache with the provided scripts.

MAGI-1 VBench

cd MotionCache4MAGI-1
bash scripts/motioncache.sh

The script uses MotionCache4MAGI-1/addconfig/config.yaml as the MotionCache configuration file.

SkyReels-V2 VBench

cd MotionCache4SkyReels-V2
bash run_vbench.sh

Please update model paths, VBench prompt paths, GPU IDs, and output directories in the scripts before running.

Citation

If you find MotionCache useful for your research, please cite:

@misc{xu2026motionawarecachingefficientautoregressive,
      title={Motion-Aware Caching for Efficient Autoregressive Video Generation},
      author={Jing Xu and Yuexiao Ma and Xuzhe Zheng and Xing Wang and Shiwei Liu and Chenqian Yan and Xiawu Zheng and Rongrong Ji and Fei Chao and Songwei Liu},
      year={2026},
      eprint={2605.01725},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2605.01725}
}

Acknowledgments

This repository builds on the following projects:

Cccccz
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