3 papers
cs.CV2025
A Style-Based Profiling Framework for Quantifying the Synthetic-to-Real Gap in Autonomous Driving Datasets
Dingyi Yao, Xinyao Han, Ruibo Ming +5
Ensuring the reliability of autonomous driving perception systems requires extensive environment-based testing, yet real-world execution is often impractical. Synthetic datasets ha…
cs.CV2025
Synthetic Dataset Evaluation Based on Generalized Cross Validation
Zhihang Song, Dingyi Yao, Ruibo Ming +3
With the rapid advancement of synthetic dataset generation techniques, evaluating the quality of synthetic data has become a critical research focus. Robust evaluation not only dri…
cs.CV2024
ARCON: Advancing Auto-Regressive Continuation for Driving Videos
Ruibo Ming, Jingwei Wu, Zhewei Huang +4
Recent advancements in auto-regressive large language models (LLMs) have led to their application in video generation. This paper explores the use of Large Vision Models (LVMs) for…