Ruibo Ming
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View article: A Style-Based Profiling Framework for Quantifying the Synthetic-to-Real Gap in Autonomous Driving Datasets
A Style-Based Profiling Framework for Quantifying the Synthetic-to-Real Gap in Autonomous Driving Datasets Open
Ensuring the reliability of autonomous driving perception systems requires extensive environment-based testing, yet real-world execution is often impractical. Synthetic datasets have therefore emerged as a promising alternative, offering a…
View article: Synthetic Dataset Evaluation Based on Generalized Cross Validation
Synthetic Dataset Evaluation Based on Generalized Cross Validation Open
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 drives innovations in data generation methods but also guides …
View article: ARCON: Advancing Auto-Regressive Continuation for Driving Videos
ARCON: Advancing Auto-Regressive Continuation for Driving Videos Open
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 video continuation, a task essential for building world mo…
View article: A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches
A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches Open
Future Frame Synthesis (FFS), the task of generating subsequent video frames from context, represents a core challenge in machine intelligence and a cornerstone for developing predictive world models. This survey provides a comprehensive a…
View article: Synthetic Datasets for Autonomous Driving: A Survey
Synthetic Datasets for Autonomous Driving: A Survey Open
Autonomous driving techniques have been flourishing in recent years while thirsting for huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up with the pace of changing requirements due to their expe…