Aether: Geometric-Aware Unified World Modeling

Aether Team, Haoyi Zhu, Yifan Wang, Zhou, Wenzheng Chang, Yang Zhou, lizizun, Junyi Chen, Chunhua Shen, Jiangmiao Pang, Tong He

Aether: Geometric-Aware Unified World Modeling: 29 upvotes on Hugging Face Daily Papers, #5 of 39 papers on 2025-03-25. Day-by-day upvote history.

The integration of geometric reconstruction and generative modeling remains a critical challenge in developing AI systems capable of human-like spatial reasoning. This paper proposes Aether, a unified framework that enables geometry-aware reasoning in world models by jointly optimizing three core capabilities: (1) 4D dynamic reconstruction, (2) action-conditioned video prediction, and (3) goal-conditioned visual planning. Through task-interleaved feature learning, Aether achieves synergistic knowledge sharing across reconstruction, prediction, and planning objectives. Building upon video generation models, our framework demonstrates unprecedented synthetic-to-real generalization despite never observing real-world data during training. Furthermore, our approach achieves zero-shot generalization in both action following and reconstruction tasks, thanks to its intrinsic geometric modeling. Remarkably, even without real-world data, its reconstruction performance far exceeds that of domain-specific models. Additionally, Aether leverages a geometry-informed action space to seamlessly translate predictions into actions, enabling effective autonomous trajectory planning. We hope our work inspires the community to explore new frontiers in physically-reasonable world modeling and its applications.

Paper page on Hugging Face · arXiv

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