Aether: Geometric-Aware Unified World Modeling
Authors: name: Aether Team, name: Haoyi Zhu, name: Yifan Wang, name: Jianjun Zhou, name: Wenzheng Chang, name: Yang Zhou, name: Zizun Li, name: Junyi Chen, name: Chunhua Shen, name: Jiangmiao Pang, name: Tong He

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.