DiLA：解耦的潜在动作世界模型
DiLA: Disentangled Latent Action World Models
AI总结：
DiLA通过内容-结构解耦解决动作抽象与生成保真度的平衡问题，实现高质量视频生成和动作迁移。
摘要：
潜在动作模型（LAMs）通过推断连续帧间的抽象动作来学习世界模型，但面临动作抽象与生成保真度的权衡问题。现有方法通常通过两阶段训练或限制预测到光流来解决。本文提出DiLA，一种解耦的潜在动作世界模型，通过内容-结构解耦解决这一权衡。我们的关键发现是解耦和潜在动作学习是共演进的：潜在动作学习中的预测瓶颈驱动解耦，迫使模型将空间布局压缩到结构路径，同时将视觉细节卸载到单独的内容路径进行生成。这种协同作用产生了一个连续且语义结构化的潜在动作空间，而不牺牲生成质量。DiLA在视频生成质量、动作迁移、视觉规划和流形可解释性方面表现优异。这些发现确立了DiLA作为统一框架，同时实现高层动作抽象和高保真生成，推动了自监督世界模型学习的前沿。
英文摘要：
Latent Action Models (LAMs) enable the learning of world models from unlabeled video by inferring abstract actions between consecutive frames. However, LAMs face a fundamental trade-off between action abstraction and generation fidelity. Existing methods typically circumvent this issue by using two-stage training with pre-trained world models or by limiting predictions to optical flow. In this paper, we introduce DiLA, a novel Disentangled Latent Action world model that aims to resolve this trade-off via content-structure disentanglement. Our key insight is that disentanglement and latent action learning are co-evolving: the predictive bottleneck inherent in latent action learning serves as a driving force for disentanglement, compelling the model to distill spatial layouts into the structure pathway while offloading visual details to a separate content pathway for generation. This synergy yields a continuous, semantically structured latent action space without compromising generative quality. DiLA achieves superior results in video generation quality, action transfer, visual planning, and manifold interpretability. These findings establish DiLA as a unified framework that simultaneously achieves high-level action abstraction and high-fidelity generation, advancing the frontier of self-supervised world model learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
