GigaWorld-0：世界模型作为数据引擎赋能具身AI
GigaWorld-0: World Models as Data Engine to Empower Embodied AI
AI总结：
GigaWorld-0通过整合视频生成与3D建模技术，构建统一的数据引擎，生成高质量具身交互数据，提升VLA模型在现实任务中的性能。
摘要：
世界模型正逐渐成为一种可扩展、数据高效的具身AI的基础范式。在本工作中，我们提出了GigaWorld-0，一个统一的世界模型框架，专门设计为视觉-语言-动作（VLA）学习的数据引擎。GigaWorld-0集成了两个协同作用的组件：GigaWorld-0-Video，利用大规模视频生成来产生多样、纹理丰富且时间上一致的具身序列，在细粒度控制外观、摄像机视角和动作语义的情况下；以及GigaWorld-0-3D，结合3D生成建模、3D高斯点云重建、物理可微系统识别和可执行运动规划，以确保几何一致性和物理真实性。它们的联合优化使能够可扩展地合成视觉引人注目、空间一致、物理合理且指令一致的具身交互数据。通过我们的高效GigaTrain框架，利用FP8精度和稀疏注意力大幅减少内存和计算需求。我们进行了全面的评估，显示GigaWorld-0在多个维度上生成高质量、多样且可控的数据。关键的是，训练于GigaWorld-0生成数据的VLA模型（例如GigaBrain-0）在现实世界中表现出色，显著提高了在物理机器人上的泛化能力和任务成功率，而无需在训练过程中进行任何现实世界交互。
英文摘要：
World models are emerging as a foundational paradigm for scalable, data-efficient embodied AI. In this work, we present GigaWorld-0, a unified world model framework designed explicitly as a data engine for Vision-Language-Action (VLA) learning. GigaWorld-0 integrates two synergistic components: GigaWorld-0-Video, which leverages large-scale video generation to produce diverse, texture-rich, and temporally coherent embodied sequences under fine-grained control of appearance, camera viewpoint, and action semantics; and GigaWorld-0-3D, which combines 3D generative modeling, 3D Gaussian Splatting reconstruction, physically differentiable system identification, and executable motion planning to ensure geometric consistency and physical realism. Their joint optimization enables the scalable synthesis of embodied interaction data that is visually compelling, spatially coherent, physically plausible, and instruction-aligned. Training at scale is made feasible through our efficient GigaTrain framework, which exploits FP8-precision and sparse attention to drastically reduce memory and compute requirements. We conduct comprehensive evaluations showing that GigaWorld-0 generates high-quality, diverse, and controllable data across multiple dimensions. Critically, VLA model (e.g., GigaBrain-0) trained on GigaWorld-0-generated data achieve strong real-world performance, significantly improving generalization and task success on physical robots without any real-world interaction during training.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
