AI 中文总结
CG-World是源自工业计算机图形流水线的大规模世界状态数据集，含约85万时间对齐片段，支持干预学习与反事实推理，经评估可为世界模型相关任务提供结构化监督，拟打造共享数据基础设施。
AI 中文摘要
世界模型必须学习状态、动作、事件与观测的联合动力学，但现有视频、机器人学及仿真数据集通常仅捕捉该结构的一部分。我们推出CG-World，这是源自工业计算机图形生产流水线的大规模世界状态数据集与协议。CG-World明确记录中间状态，包括多模态语义、空间结构、骨骼与控制器状态、运动曲线、相机与光照参数、物理缓存、接触事件及多通道渲染。CG-World v1包含约85万个时间对齐的1-5秒片段，它将潜在状态、观测、关系、事件及分支元数据分离，并组织为统一的时空样本。为支持干预学习与反事实推理，CG-World定义了涵盖事实轨迹、观测干预、动作干预、机制干预及严格反事实分支的分支谱系，明确记录干预目标、不变量及替代结果。我们在几何条件视频生成、动作预测及闭环视觉-语言-动作策略迁移任务上评估该数据集，结果显示CG-World为可控生成、动作建模及具身策略迁移提供了可复用的结构化监督。我们计划通过持续数据收集与社区合作扩展CG-World，打造面向世界模型、物理AI及具身智能的共享数据基础设施。
英文摘要
World models must learn the joint dynamics of states, actions, events, and observations, yet existing video, robotics, and simulation datasets usually capture only part of this structure. We introduce CG-World, a large-scale world-state dataset and protocol derived from industrial computer graphics production pipelines. CG-World explicitly records intermediate states, including multimodal semantics, spatial structure, skeletal and controller states, motion curves, camera and lighting parameters, physics caches, contact events, and multi-pass renderings. CG-World v1 contains approximately 850,000 temporally aligned segments of 1-5 seconds. It separates latent states, observations, relations, events, and branch metadata, and organizes them into unified spatiotemporal samples. To support intervention learning and counterfactual reasoning, CG-World defines a branch lineage covering factual trajectories, observation interventions, action interventions, mechanism interventions, and strict counterfactual branches, with intervention targets, invariants, and alternative outcomes explicitly recorded. We evaluate the dataset on geometry-conditioned video generation, action prediction, and closed-loop vision-language-action policy transfer. Results show that CG-World provides reusable structured supervision for controlled generation, action modeling, and embodied policy transfer. We plan to expand CG-World through continued data collection and community collaboration toward a shared data infrastructure for world models, Physical AI, and embodied intelligence.
