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cs.CV2026

UNIVERSE: Unified Video Action Models for Autonomous Driving with Flexible Mask-Modulated Modality Generation

Mengmeng Liu, Diankun Zhang, Jiuming Liu +7

World Action Models (WAMs) have shown strong potential for improving action generalization in autonomous driving by using future video prediction as dense supervision for scene dyn…

cs.CV2026

DriveVA: Video Action Models are Zero-Shot Drivers

Mengmeng Liu, Diankun Zhang, Jiuming Liu +7

Generalization is a central challenge in autonomous driving, as real-world deployment requires robust performance under unseen scenarios, sensor domains, and environmental conditio…

cs.CV2026

L2D2-GS: Learning to Densify for Feedforward Dynamic Gaussian Scene Reconstruction

Zetian Song, Chenming Wu, Junnan Liu +6

High-fidelity reconstruction of dynamic urban environments is a cornerstone of autonomous driving simulation and large-scale world modeling. While 3D Gaussian Splatting (3DGS) has…

cs.CV2026

ReWorld: Representation Learning for World Action Models

Tianze Xia, Lijun Zhou, Kaixin Xiong +9

World Action Models (WAMs) unify future environment prediction with action generation for autonomous driving, yet existing approaches optimize only the final outputs, leaving inter…

cs.CV2026

CausalDrive: Real-time Causal World Models for Autonomous Driving

Tianyi Yan, Huan Zheng, Dubing Chen +10

World models have emerged as a promising paradigm for scaling autonomous driving (AD) data, yet existing video generative models fall short as interactive simulators. Layout-condit…

cs.CV2026

From Pairs to Sequences: Track-Aware Policy Gradients for Keypoint Detection

Yepeng Liu, Hao Li, Liwen Yang +8

Keypoint-based matching is a fundamental component of modern 3D vision systems, such as Structure-from-Motion (SfM) and SLAM. Most existing learning-based methods are trained on im…