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20192026
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cs.CV2026

GeoWAM: Visual Geometry World Action Models for Autonomous Driving

Yiren Lu, Xin Ye, Jiaming Liu +10

World action models (WAMs) have recently gained increasing attention as a framework for jointly modeling scene evolution and ego actions in autonomous driving. Most existing WAMs l…

cs.CV2026

TraVEL: Trajectory-Guided Video Embedding Learning for Driving-Video Retrieval

Yi-Chung Chen, Philip Jacobson, Tom Lampo +6

Efficiently retrieving relevant clips from large-scale driving logs is essential for data curation, model development, and safety analysis. Structured and rule-based retrieval syst…

cs.CV2026

Structured 3D Latents Are Surprisingly Powerful: Unleashing Generalizable Style with 2D Diffusion

Yiran Qiao, Yiren Lu, Yunlai Zhou +5

3D asset generation plays a pivotal role in fields such as gaming and virtual reality, enabling the rapid synthesis of high-fidelity 3D objects from a single or multiple images. Bu…

cs.CV2026

AdvSplat: Adversarial Attacks on Feed-Forward Gaussian Splatting Models

Yiran Qiao, Yiren Lu, Yunlai Zhou +4

3D Gaussian Splatting (3DGS) is increasingly recognized as a powerful paradigm for real-time, high-fidelity 3D reconstruction. However, its per-scene optimization pipeline limits s…

cs.CV2026

Reconstruction Matters: Learning Geometry-Aligned BEV Representation through 3D Gaussian Splatting

Yiren Lu, Xin Ye, Burhaneddin Yaman +4

Bird's-Eye-View (BEV) perception serves as a cornerstone for autonomous driving, offering a unified spatial representation that fuses surrounding-view images to enable reasoning fo…

cs.CV2026

GSMem: 3D Gaussian Splatting as Persistent Spatial Memory for Zero-Shot Embodied Exploration and Reasoning

Yiren Lu, Yi Du, Disheng Liu +3

Effective embodied exploration requires agents to accumulate and retain spatial knowledge over time. However, existing scene representations, such as discrete scene graphs or stati…