Xiaomi Auto World Model 2.0：実世界運転と身体性知能のための因果世界モデル
Xiaomi Auto World Model 2.0: Causal World Models for Real-World Driving and Embodied Intelligence
マルチビュー時系列から3Dガウシアンを再構成し、因果的な物理進化を生成する統合ストリーミング世界モデルを提案。運転や身体性AIの閉ループシミュレーションに応用する。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Zhenxin Zhu, Zhijian Huang, Cheng Chi, Yuru Wang, Jianhua Wu, Jiayi Guan, Yonggang Li, Mingfei Tu, JiaKui Hu, Tao Xu, Xianqi Wang, Hao Li, Yuechuan Pu, Qizeng Jia, Ruiling Yang, Hongxiao Yu, Xuhan Sheng, Jian Mao, Ruiqiang Zhang, Yu Zhu, Guang Chen, Hangjun Ye, Bing Wang
分類: cs.AI, cs.LG
原文アブストラクト
We present Xiaomi Auto World Model 2.0, a unified streaming causal world model for Physical AI. It models and generates causal physical evolution in the real world, and unifies heterogeneous conditioning inputs for view-consistent generation. The architecture integrates View-Temporal Attention (VTA) to capture cross-view spatial dependencies and temporal dynamics, together with Multi-view Rotary Position Encoding (RoPE) to encode positional relationships across views and time. WorldRec 2.0 reconstructs static and dynamic 3D Gaussians from multi-view, multi-temporal observations in a single forward pass and uses their renderings as explicit scene context to preserve geometry and appearance during long-horizon generation. Conditioning inputs are fused through VAE latent concatenation in a shared representation space. We incorporate Gated Linear Attention (GLA) into the temporal layers of VTA for efficient streaming inference, reducing computation and KV-cache memory during multi-view rollouts. A physical-interaction data pipeline covers diverse interactions and long-tailed events. From Pre-train onward, these data are combined with large-scale videos and heterogeneous visual modalities to learn visual, geometric, and dynamic priors. During Mid-train, training across diverse view configurations further improves the model's ability to capture how actions and object states shape subsequent scene evolution. After distillation-based Post-train, we apply the model to closed-loop simulation, closed-loop training, and World Action Model (WAM) applications. These applications generate physically consistent future states and support world-state prediction, policy learning, and action generation for Physical AI.