EgoGenesis: Egocentric World-Action Modeling with Online Anchored Projective Memory and Action-3D RoPE

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Zexuan Yan, Yuzhou Wu, Yue Ma, Zonghang He, Kaibo Yin, Xiaobing Tu, Yinggui Wang, Jinkui Ren, Xiantao Zhang, Shijian Wang, Jinghong Liu, Linfeng Zhang
• Venue: arXiv: cs.CV
• Published: 2026-07-30
• Collected: 2026-07-31
• URL: http://arxiv.org/abs/2607.28243v1
• PDF: https://arxiv.org/pdf/2607.28243v1
• DOI: UNKNOWN

Plain-Language Brief

The research presents a new method, EgoGenesis, for generating high-quality manipulation videos in an egocentric world for training embodied AI. The method uses a pretrained video generation prior and introduces two geometry-aware conditioning mechanisms: Online Anchored Projective Memory (OAPM) and Action-3D Rotary Position Embedding (A3D-RoPE). These components improve visual fidelity, geometric stability, and action alignment in long egocentric rollouts. The generated data improves downstream WAM generalization when augmenting real trajectories.

What the Document Says

• The paper presents a method (EgoGenesis) for generating high-quality manipulation videos in an egocentric world to expand scarce real-world training data.
• EgoGenesis builds on a pretrained video generation prior and introduces two components: Online Anchored Projective Memory (OAPM) and Action-3D Rotary Position Embedding (A3D-RoPE).
• OAPM preserves a first-frame 3D scene anchor while periodically refreshing a recent state during autoregressive generation.
• A3D-RoPE encodes end-effector motion with camera-aware 3D rotary coordinates, injecting action geometry into skeleton-to-video cross-attention for precise control.
• The components improve visual fidelity, geometric stability, and action alignment in long egocentric rollouts.

Key Concepts Mentioned

• EgoGenesis
• egocentric video
• manipulation videos
• embodied AI
• pretrained video generation prior
• Online Anchored Projective Memory (OAPM)
• Action-3D Rotary Position Embedding (A3D-RoPE)
• autoregressive generation
• skeleton-to-video cross-attention

Claims Made by the Document

• EgoGenesis improves visual fidelity, geometric stability, and action alignment in long egocentric rollouts.

Evidence Used

• The paper describes the components of EgoGenesis and their effects on generated videos.
• Results show improved performance on downstream WAM generalization when augmenting real trajectories with synthesized data.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Egocentric video offers rich manipulation experience for embodied AI, yet collecting diverse egocentric data across scenes, objects, motions, and embodiments remains costly. We present \method, an egocentric world-action simulator that synthesizes controllable, high-quality manipulation videos to expand scarce real-world training data. \method{} builds on a pretrained video generation prior and introduces two geometry-aware conditioning mechanisms. Online Anchored Projective Memory (OAPM) preserves a first-frame 3D scene anchor while periodically refreshing a recent state during autoregressive generation. Action-3D Rotary Position Embedding (A3D-RoPE) encodes end-effector motion with camera-aware 3D rotary coordinates, injecting action geometry into skeleton-to-video cross-attention for precise control. Together, these components improve visual fidelity, geometric stability, and action alignment in long egocentric rollouts. Moreover, augmenting 400 real trajectories with 400 \method-generated trajectories improves out-of-distribution real-robot success from 77% to 84% on single-arm tasks and from 53% to 70% on dual-arm tasks, demonstrating that the synthesized data substantially improve downstream WAM generalization.