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CausalAlign: Causal Alignment of Video Generation Models for Consistent Ego-Motion

Kai Zhu, Yintianrun Zhang, Shuwei Jiang, Ruolin Zuo, Yixin Chen, Duomin Wang, Miao Liu, Yin Li, Daquan Zhou, Yiwu Zhong · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29915 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

基于扩散的视频生成模型(VGMs)可以生成可信的视觉视频,但他们的摄像机自我运动往往在长的地平线上流动。摄像机自我运动决定了视角如何在框架之间发生变化,并且对于保持一致的地理和轨迹至关重要。为了解决这个问题,我们提出了CausalAlign,一个具有两个组件的因果调节框架。首先,我们诊断了在VGMs中使用线性探测器和因果干预来代表、使用和保留精确的自我运动,揭示了在消化过程中上升和下降的模式。其次,我们通过将识别的层次中的代表与三维模型基础的对齐,在VGM中加强了自我运动状态。本空间通过连续框架之间的任务进行编码,使对比

Machine translation (Meta M2M100); the English original follows.

Abstract

Diffusion-based video generation models (VGMs) can produce visually plausible videos, yet their camera ego-motion often drifts over long horizons. Camera ego-motion determines how the viewpoint changes between frames and is essential for maintaining consistent geometry and trajectories. To address this problem, we propose CausalAlign, a causal alignment framework with two components. First, we diagnose how precise ego motion is represented, used, and retained within VGMs using linear probes and causal interventions, revealing a rise-and-fall pattern during denoising. Second, we strengthen ego-motion state in VGM by aligning the representations at the identified layers to a 3D foundation model through delta-token space. This space encodes the changes between consecutive frames, allowing the alignment to focus on ego-motion dynamics rather than static scene content. With Wan2.2 fine-tuned on DL3DV and RealEstate10K, CausalAlign can largely improve motion accuracy, geometric consistency, and temporal smoothness, while preserving visual quality. For example, it improves geometric consistency by more than 20% relative to the base model. Moreover, with the fine-tuned VGM, CausalAlign improves the success rate of world action models (WAMs) by 10 points on mobile manipulation tasks of RoboCasa365 benchmark. Overall, CausalAlign improves ego-motion consistency in diffusion-based VGMs and yields noticeable gains in embodied models.

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Topics: Computer vision, Artificial intelligence, Consistency (knowledge bases), Computer science, Motion (physics), Focus (optics)

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