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20232026
most citedLLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning

7 citations · 11 across the 33 of their papers we have counts for

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Showing 2025 · cs.CVShow all

6 papers · 2 filters

cs.CV2025

LangDriveCTRL: Natural Language Controllable Driving Scene Editing with Multi-modal Agents

Yun He, Francesco Pittaluga, Ziyu Jiang +3

LangDriveCTRL is a natural-language-controllable framework for editing real-world driving videos to synthesize diverse traffic scenarios. It represents each video as an explicit 3D…

cs.CV2025

AutoScape: Geometry-Consistent Long-Horizon Scene Generation

Jiacheng Chen, Ziyu Jiang, Mingfu Liang +5

This paper proposes AutoScape, a long-horizon driving scene generation framework. At its core is a novel RGB-D diffusion model that iteratively generates sparse, geometrically cons…

cs.CV2025

iFinder: Structured Zero-Shot Vision-Based LLM Grounding for Dash-Cam Video Reasoning

Manyi Yao, Bingbing Zhuang, Sparsh Garg +4

Grounding large language models (LLMs) in domain-specific tasks like post-hoc dash-cam driving video analysis is challenging due to their general-purpose training and lack of struc…

cs.CV2025

ST-VLM: Kinematic Instruction Tuning for Spatio-Temporal Reasoning in Vision-Language Models

Dohwan Ko, Sihyeon Kim, Yumin Suh +4

Spatio-temporal reasoning is essential in understanding real-world environments in various fields, eg, autonomous driving and sports analytics. Recent advances have improved the sp…

cs.CV2025

DWIM: Towards Tool-aware Visual Reasoning via Discrepancy-aware Workflow Generation & Instruct-Masking Tuning

Fucai Ke, Vijay Kumar B G, Xingjian Leng +6

Visual reasoning (VR), which is crucial in many fields for enabling human-like visual understanding, remains highly challenging. Recently, compositional visual reasoning approaches…

cs.CV2025★ 1 cited

Materialist: Physically Based Editing Using Single-Image Inverse Rendering

Lezhong Wang, Duc Minh Tran, Ruiqi Cui +5

Achieving physically consistent image editing remains a significant challenge in computer vision. Existing image editing methods typically rely on neural networks, which struggle t…