3 accepted papers
Real-time, high-fidelity reconstruction of dynamic driving scenes is challenged by complex dynamics and sparse views, with prior methods struggling to balance quality and efficiency. We propose DrivingScene, an online, feed-forward framework that reconstructs 4D dynamic scenes from only two consecut…
Reconstructing dynamic 3D urban scenes is crucial for autonomous driving, yet current methods face a stark trade-off between fidelity and computational cost. This inefficiency stems from their semantically agnostic design, which allocates resources uniformly, treating static backgrounds and safety-c…
Existing text-to-image models often rely on parame- ter fine-tuning techniques such as Low-Rank Adaptation (LoRA) to customize visual attributes. However, when com- bining multiple LoRA models for content-style fusion tasks, unstructured modifications of weight matrices often lead to undesired featu…