光の相互作用：インタラクティブビデオワールドモデルのための学習不要推論高速化
Light Interaction: Training-Free Inference Acceleration for Interactive Video World Models
インタラクティブなビデオワールドモデルの推論を高速化する、学習不要のフレームワークを提案。適応的なコンテキスト管理とキャッシュ、スパースアテンションにより最大2.59倍の高速化を実現。
著者: Jiacheng Lu, Haoyi Zhu, Sipei Yi, Enze Xie, Yu Li, Cheng Zhuo
分類: cs.CV, cs.LG
原文アブストラクト
Interactive video world models generate video chunk by chunk in response to user-controlled camera movements, enabling applications such as real-time game simulation, virtual scene navigation, and embodied AI training. However, scaling to long interactive trajectories is prohibitively expensive due to growing context memory, quadratic attention complexity, and repeated denoising steps. We present Light Interaction, a training-free inference acceleration framework for interactive video world models. Our key insight is that interaction naturally enables trajectory-dependent adaptive computation: retrieved spatial memory can be discarded during novel exploration, temporal context can be adjusted according to local latent dynamics, and early-step model outputs can be reused when the camera revisits familiar regions. Based on this insight, Light Interaction combines adaptive context management, denoising cache acceleration, and hardware-software co-designed 3D block sparse attention with fused Triton kernels. Evaluated on HY-WorldPlay and Matrix-Game-3.0, Light Interaction achieves up to 2.59x speedup without model retraining while maintaining competitive visual quality.