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COMPETITION_DESC.md

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 ## Motivation
 Given the safety concerns and high costs associated with real-world autonomous driving testing, high-
-fidelity simulation techniques have become crucial for advancing the capabilities of autonomous systems.
+fidelity simulation techniques have become crucial for advancing the capabilities of autonomous systems. 
+
+This workshop seeks to answer the following questions:
+1. How well can we Render? While NVS methods have made significant progress in generating
+photorealistic urban scenes, their performance still lags in extrapolated viewpoints when only a
+limited viewpoint is provided during training. However, extrapolated viewpoints are essential for
+closed-loop simulation. Improving the accuracy and consistency of NVS across diverse viewing
+angles is critical for ensuring that these simulators provide reliable environments for driving
+evaluation.
+2. How well can we Drive? Despite challenges in extrapolated viewpoint rendering, existing
+methods enable photorealistic simulators with reasonable performance when trained on dense
+views. These NVS-based simulators allow autonomous driving models to be
+tested in a fully closed-loop manner, bridging the gap between real-world data and interactive
+evaluation. This shift allows for benchmarking autonomous driving algorithms under realistic
+conditions, overcoming the limitations of static datasets.
+
+This competition focus on the second question. If you have interest on the first one, please refer to [this link](https://huggingface.co/spaces/XDimLab/ICCV2025-RealADSim-NVS)
 
 ## Task Description
 In this competition, we evaluate the performance of submitted autonomous driving algorithms on a closed-loop simulator, with multiple challenging scenarios. Both simulator and submitted autonomous driving algorithms will execute online to make sure the closed-loop evaluation. Models and running environments are expected to be submitted to this reason. If you have concern about the privacy, please refer to the privacy section to check how we protect your privacy.