CVPR 2026 | 时空时序论文总结（天气预报，交通模拟，域自适应等）

CVPR 2026将在2026年6月5日至7日于美国丹佛科罗拉多会议中心（Colorado Convention Center）举行。

本文总结了CVPR 2026上有关时空时序的相关论文。

时空时序Topic：天气预报，交通模拟，域自适应等。

注：鉴于自动驾驶、空间智能及视频时空建模等领域文献多，为保持论述聚焦，本总结仅围绕天气预报与交通模拟这两大方向展开时空数据相关工作的梳理

CVPR2026论文列表：https://cvpr.thecvf.com/virtual/2026/papers.html

点击文末阅读原文跳转笔者知乎链接（跳转论文链接更方便）

1 RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning

链接：https://cvpr.thecvf.com/virtual/2026/poster/37597

作者：Ehsan Ahmadi, Hunter Schofield, Behzad Khamidehi, Fazel Arasteh, Jinjun Shan, Lili Mou, Dongfeng Bai, Kasra Rezaee

关键词：多智能体交通模拟，强化学习微调

2 Spatiotemporal Pyramid Flow Matching for Climate Emulation

链接：https://cvpr.thecvf.com/virtual/2026/poster/37833

arXiv：http://arxiv.org/abs/2512.02268

代码：https://github.com/stanfordmlgroup/spf

作者：Jeremy Irvin, Jiaqi Han, Zikui Wang, Abdulaziz Alharbi, Yufei Zhao, Nomin-Erdene Bayarsaikhan, Daniele Visioni, Andrew Y. Ng, Duncan Watson-Parris

关键词：气候模拟，流匹配

3 ChangeBridge: Spatiotemporal Image Generation with Multimodal Controls for Remote Senisng

链接：https://cvpr.thecvf.com/virtual/2026/poster/37327

arXiv：https://arxiv.org/abs/2507.04678

代码：https://github.com/zhenghuizhao/ChangeBridge

作者：Zhenghui Zhao, Chen Wu, Xiangyong Cao, Di Wang, Hongruixuan Chen, Datao Tang, Liangpei Zhang, Zhuo Zheng

关键词：时空图像生成，扩散模型，遥感

4 PFGNet: A Fully Convolutional Frequency-Guided Peripheral Gating Network for Efficient Spatiotemporal Predictive Learning

链接：https://cvpr.thecvf.com/virtual/2026/poster/39501

arXiv：https://arxiv.org/abs/2602.20537

代码：https://github.com/fhjdqaq/PFGNet

作者：Xinyong Cai, Changbin Sun, Yong Wang, Hongyu Yang, Yuankai Wu

关键词：时空预测学习，全卷积框架

5 MeteorPred: A Meteorological Multimodal Large Model and Dataset for Severe Weather Event Prediction

链接：https://cvpr.thecvf.com/virtual/2026/poster/36482

arXiv：https://arxiv.org/abs/2508.06859

作者：Shuo Tang, Jian Xu, Jiadong Zhang, yi chen, Qizhao Jin, Lingdong Shen, Cheng-Lin Liu, Shiming Xiang

关键词：天气事件预报，benchmark，多模态大模型

6 TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation

链接：https://cvpr.thecvf.com/virtual/2026/poster/37456

代码：https://github.com/TrafficComposer/TrafficAlign

作者：Zhi Tu, Liangkun Niu, Tianyi Zhang

关键词：交通场景生成，LLM，对齐

7 STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting

链接：https://cvpr.thecvf.com/virtual/2026/poster/39880

arXiv：http://arxiv.org/abs/2509.25210

代码：https://github.com/chenhao-zju/STCast

作者：Hao Chen, Tao Han, Jie ZHANG, Song Guo, Lei Bai

关键词：天气预报

EarthAi: STCast：面向全球与区域天气预报的自适应边界对齐方法

8 Towards Uncertainty-aware Unsupervised Domain Adaptation for Videos and Time-Series with Causal Optimal Transport

链接：https://cvpr.thecvf.com/virtual/2026/poster/38445

作者：Khushboo Mishra, Varun Trivedi, Tanima Dutta

关键词：无监督域自适应，最优传输，因果