DriVerse is a generative model that simulates driving scenes from an image and trajectory using multimodal prompting and motion alignment, achieving better performance on nuScenes and Waymo datasets with minimal training.
High-resolution image synthesis with latent diffusion models
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2025 2verdicts
UNVERDICTED 2representative citing papers
GCLIP improves TF-OVSS by reshaping last-block attention via fusion of global-token block attention with Query-Query attention and applying channel suppression to Value embeddings, outperforming prior methods on five benchmarks.
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DriVerse: Navigation World Model for Driving Simulation via Multimodal Trajectory Prompting and Motion Alignment
DriVerse is a generative model that simulates driving scenes from an image and trajectory using multimodal prompting and motion alignment, achieving better performance on nuScenes and Waymo datasets with minimal training.
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Rethinking the Global Knowledge of CLIP in Training-Free Open-Vocabulary Semantic Segmentation
GCLIP improves TF-OVSS by reshaping last-block attention via fusion of global-token block attention with Query-Query attention and applying channel suppression to Value embeddings, outperforming prior methods on five benchmarks.