title: Aether: Geometric-Aware Unified World Modeling
summary: 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_authors._id: 67e22eca9455abdd1d257263
paper_authors.name: Aether Team
paper_authors._id: 67e22eca9455abdd1d257264
paper_authors.name: Haoyi Zhu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T09:15:13.586Z
paper_authors._id: 6283546209aa80237c6c482c
paper_authors.avatarUrl: /avatars/0d6fc5846c0456d5282d82d5bf4d7056.svg
paper_authors.fullname: Haoyi Zhu
paper_authors.type: user
paper_authors.user: HaoyiZhu
paper_authors._id: 67e22eca9455abdd1d257265
paper_authors.name: Yifan Wang
paper_authors._id: 67e22eca9455abdd1d257266
paper_authors.name: Jianjun Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T09:05:54.719Z
paper_authors._id: 667e81565934c9fae29207ef
paper_authors.avatarUrl: /avatars/431e777c71fccf7cf48ce013e5f6f1cb.svg
paper_authors.fullname: Zhou
paper_authors.type: user
paper_authors.user: ZhouTimeMachine
paper_authors._id: 67e22eca9455abdd1d257267
paper_authors.name: Wenzheng Chang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T08:20:10.947Z
paper_authors._id: 67a5b0fe5a8652514e67c38c
paper_authors.avatarUrl: /avatars/28da8e93ee00fd77c7e62d16f9b94045.svg
paper_authors.fullname: Wenzheng Chang
paper_authors.type: user
paper_authors.user: AmberHeart
paper_authors._id: 67e22eca9455abdd1d257268
paper_authors.name: Yang Zhou
paper_authors._id: 67e22eca9455abdd1d257269
paper_authors.name: Zizun Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T09:16:06.912Z
paper_authors._id: 65e7eb86c7a0617cc71d3df4
paper_authors.avatarUrl: /avatars/01020b6b5ccb08bf8aa10fd5f8b2701d.svg
paper_authors.fullname: lizizun
paper_authors.type: user
paper_authors.user: lizizun
paper_authors._id: 67e22eca9455abdd1d25726a
paper_authors.name: Junyi Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T09:16:13.751Z
paper_authors._id: 6679bb85972a0f224cde335c
paper_authors.avatarUrl: /avatars/bf0649645458e206ba5224b001723641.svg
paper_authors.fullname: Junyi Chen
paper_authors.type: user
paper_authors.user: Junyichen
paper_authors._id: 67e22eca9455abdd1d25726b
paper_authors.name: Chunhua Shen
paper_authors._id: 67e22eca9455abdd1d25726c
paper_authors.name: Jiangmiao Pang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T09:16:26.855Z
paper_authors._id: 65783ee6ee33d547aecc3ffc
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65783ee6ee33d547aecc3ffc/lWZX88c-0dCsN-yB9Jhlf.jpeg
paper_authors.fullname: Jiangmiao Pang
paper_authors.type: user
paper_authors.user: Jiangmiao
paper_authors._id: 67e22eca9455abdd1d25726d
paper_authors.name: Tong He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T09:16:35.824Z
paper_authors._id: 64478c64e2148488340229db
paper_authors.avatarUrl: /avatars/f5c23489a068e896381cdc25836ce3dd.svg
paper_authors.fullname: he
paper_authors.type: user
paper_authors.user: tonghe
paper_title: Aether: Geometric-Aware Unified World Modeling
paper_summary: 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_ai_summary: Aether is a unified framework that combines 4D dynamic reconstruction, action-conditioned video prediction, and goal-conditioned visual planning, achieving zero-shot generalization and superior reconstruction without real-world data.
paper_ai_keywords: 4D dynamic reconstruction action-conditioned video prediction goal-conditioned visual planning task-interleaved feature learning synthetic-to-real generalization zero-shot generalization intrinsic geometric modeling geometry-informed action space autonomous trajectory planning