将视频扩散模型拆解为运动与外观两阶段，高效构建可控驾驶世界模型。
MAD: Motion Appearance Decoupling for efficient Driving World Models
- 分两阶段：先学结构化运动，再生成真实视觉外观。
- 用不到6%计算量就达到当前最优效果，支持文本、视角和物体控制。
- 适合需要高效可控驾驶模拟的自动驾驶研发人员。
近期视频扩散模型能生成逼真且时间连贯的视频，但在自动驾驶所需的高度结构化运动与物理一致性方面仍不足。将通用视频模型适配到驾驶领域虽有前景，但通常需大量领域数据和昂贵微调。本文提出一种高效适配框架，将通用视频扩散模型转化为可控制的驾驶世界模型，仅需少量监督。核心思路是解耦运动学习与外观合成：首先在简化形式下训练模型预测骨架化车辆与场景元素的结构化运动，聚焦于物理与社会合理性；随后复用同一骨干网络，根据这些运动序列生成真实的RGB视频，实现“动态穿衣”。该两阶段过程模仿推理-渲染范式。实验表明，该解耦方法极为高效：以SVD为例，仅用不到6%的计算量即达到现有最优性能。扩展至LTX后，MAD-LTX模型超越所有开源竞品，并支持文本、自车视角及物体控制。项目主页：https://vita-epfl.github.io/MAD-World-Model/
原文摘要 · Abstract (English)
Recent video diffusion models generate photorealistic, temporally coherent videos, yet they fall short as reliable world models for autonomous driving, where structured motion and physically consistent interactions are essential. Adapting these generalist video models to driving domains has shown promise but typically requires massive domain-specific data and costly fine-tuning. We propose an efficient adaptation framework that converts generalist video diffusion models into controllable driving world models with minimal supervision. The key idea is to decouple motion learning from appearance synthesis. First, the model is adapted to predict structured motion in a simplified form: videos of skeletonized agents and scene elements, focusing learning on physical and social plausibility. Then, the same backbone is reused to synthesize realistic RGB videos conditioned on these motion sequences, effectively "dressing" the motion with texture and lighting. This two-stage process mirrors a reasoning-rendering paradigm: first infer dynamics, then render appearance. Our experiments show this decoupled approach is exceptionally efficient: adapting SVD, we match prior SOTA models with less than 6% of their compute. Scaling to LTX, our MAD-LTX model outperforms all open-source competitors, and supports a comprehensive suite of text, ego, and object controls. Project page: https://vita-epfl.github.io/MAD-World-Model/
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