DriveLaW unifies video world modeling and trajectory planning by injecting video-generator latents into a diffusion planner, achieving SOTA video prediction and a new record on the NAVSIM planning benchmark.
Street gaussians: Modeling dynamic urban scenes with gaussian splatting
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 3verdicts
UNVERDICTED 3representative citing papers
Unposed-to-3D learns simulation-ready 3D vehicle models from unposed real images by predicting camera parameters for photometric self-supervision, then adding scale prediction and harmonization.
GeoPredict improves VLA manipulation accuracy by adding predictive kinematic trajectories and 3D Gaussian workspace geometry as training-time depth-rendering supervision.
citing papers explorer
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DriveLaW:Unifying Planning and Video Generation in a Latent Driving World
DriveLaW unifies video world modeling and trajectory planning by injecting video-generator latents into a diffusion planner, achieving SOTA video prediction and a new record on the NAVSIM planning benchmark.
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Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images
Unposed-to-3D learns simulation-ready 3D vehicle models from unposed real images by predicting camera parameters for photometric self-supervision, then adding scale prediction and harmonization.
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GeoPredict: Leveraging Predictive Kinematics and 3D Gaussian Geometry for Precise VLA Manipulation
GeoPredict improves VLA manipulation accuracy by adding predictive kinematic trajectories and 3D Gaussian workspace geometry as training-time depth-rendering supervision.