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Generating missing multi-view data from diverse driving videos boosts closed-loop driving robustness in edge cases by over 30%.
HCC-STAR not only surpasses leading models in treatment accuracy but also offers a significant survival advantage, highlighting the potential of AI in precision oncology.
Lateral string stability can make or break the safety of CAV platoons, and V2V communication is the game-changer that ensures error attenuation.
Introducing a transport-based approach that ensures stable and expressive 3D stylizations by controlling style feature allocation across multiple views.
Achieving state-of-the-art identity fidelity and temporal stability, TIGER redefines the standards for high-quality face video restoration.
DrivingGen reveals that current generative driving world models either look good but break physics, or capture motion realistically but lack visual fidelity, exposing a critical trade-off for autonomous driving applications.
