AuroraWorld：面向机器人操作的从外视角到自我视角视频生成的结构化 4D 世界建模
AuroraWorld: Structured 4D World Modeling for Exo-to-Ego Video Generation in Robot Manipulation
AI 导读
arXiv 论文 AuroraWorld 提出结构化 4D 世界建模方法，用于在机器人操作任务中从外视角视频合成自我视角视频。论文指出自我视角观测能捕捉接触交互与物体状态变化，有利于视觉运动策略学习，但大规模采集成本高。该方法旨在解决大幅视角变换、严重遮挡和复杂物体交互下合成物理一致且与策略相关的自我视角视频的难题。
正文 · 原文
Abstract:Egocentric observations provide a robot-centric view of manipulation processes, capturing contact interactions and object-state changes that are important for visuomotor policy learning but difficult to observe from external cameras. However, collecting such data at scale remains expensive due to additional hardware requirements and platform-specific deployment costs. Synthesizing egocentric observations from large-scale exocentric manipulation videos provides a scalable alternative. However, existing methods struggle to synthesize physically consistent and policy-relevant egocentric videos under substantial viewpoint transformations, severe occlusions, complex object interactions, and inherently sparse observations from available exocentric this http URL will be made publicly available at this https URL
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2611.00177 [cs.AI] |
| (or arXiv:2611.00177v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.00177 arXiv-issued DOI via DataCite |
Submission history
From: Hao Xue [view email]
[v1]
Fri, 30 Oct 2026 14:50:06 UTC (8,534 KB)
[v2]
Wed, 4 Nov 2026 15:38:24 UTC (6,099 KB)
来源：arXiv cs.AI · arxiv.org