arXiv cs.AI· Hao Xue, Peng-Fei Zhang, Ying Cheng, Lanyun Zhu, Lei Zhu, Heng Tao Shen·· 11 小时前AI 评分31
AuroraWorld：面向机器人操作外视角到内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
AuroraWorld 提出结构化 4D 世界建模方法，可从大规模外视角机器人操作视频合成内视角观测，用于视觉运动策略学习。该方法针对大幅视角变换、严重遮挡、复杂物体交互和外视角观测稀疏等挑战，目标是生成物理一致且与策略相关的内视角视频。论文未披露模型规模与 benchmark 分数，代码将公开发布。
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