Newest papers

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

Japanese

拡散ベースのビデオ生成モデル(VGMs)は視覚的に信頼できるビデオを生成することができるが、カメラのエゴモーションはしばしば長い地平線を横断する。カメラエゴモーションは、フレーム間の視点がどのように変化するかを決定し、一貫した地理学と軌道を維持するために不可欠である。この問題を解決するために、我々は2つのコンポーネントを含む因果関係の枠組みであるCausalAlignを提案する。第一に、我々は、正確なエゴモーションがどのように表され、使用され、継続的なフレーム間のタスクと因果関係の介入を使用してVGMs内に維持されるかを診断する。第二に、我々は、識別された層

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.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

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

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange