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

Paper recorded by Signals 4 on 2026-10-21 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-21 on arXiv · recorded by Signals 4 on 2026-10-22

Category: cs.CV · 计算机视觉 · first seen 2026-10-22

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 dia

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#203 most recent of 756 cs.CV papers we have recorded · ↑ newer: DewHand: Direct Metric World-Space Hand Motion Reconstruction from Ego · ↓ older: Asking a Multimodal LLM Whether Someone Fell: Zero-Shot Screening of L
Cite this page: CausalAlign: Causal Alignment of Video Generation Models for Consistent Ego-Motion: the #203 most recent of 756 cs.CV papers we have recorded (as of 2026-10-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/causalalign-causal-alignment-of-video-generation-models-for-consistent-ego-motio.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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