Ground4D resolves temporal conflicts in feedforward 4D Gaussian reconstruction for off-road scenes via voxel-grounded temporal aggregation with intra-voxel softmax and surface normal regularization, outperforming prior methods on ORAD-3D and RELLIS-3D while generalizing zero-shot.
Drivingrecon: Large 4d gaussian reconstruction model for autonomous driving
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VAG is a synchronized dual-stream flow-matching framework that generates aligned video-action pairs for synthetic embodied data synthesis and policy pretraining.
A coupled world-agent framework uses 3D Gaussian reconstruction and first-person RGB-D perception with iterative planning to enable goal-directed, collision-avoiding humanoid behavior in novel reconstructed scenes.
SpectralSplat disentangles appearance from geometry in feed-forward 3D Gaussian Splatting by factoring color into base and adapted streams conditioned on DINOv2 embeddings, trained on paired data from a hybrid relighting pipeline.
Flux4D reconstructs large-scale dynamic 4D scenes unsupervised by predicting moving 3D Gaussians from photometric losses and static regularization when trained across multiple scenes.
citing papers explorer
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VAG: Dual-Stream Video-Action Generation for Embodied Data Synthesis
VAG is a synchronized dual-stream flow-matching framework that generates aligned video-action pairs for synthetic embodied data synthesis and policy pretraining.
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Visually-grounded Humanoid Agents
A coupled world-agent framework uses 3D Gaussian reconstruction and first-person RGB-D perception with iterative planning to enable goal-directed, collision-avoiding humanoid behavior in novel reconstructed scenes.
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SpectralSplat: Appearance-Disentangled Feed-Forward Gaussian Splatting for Driving Scenes
SpectralSplat disentangles appearance from geometry in feed-forward 3D Gaussian Splatting by factoring color into base and adapted streams conditioned on DINOv2 embeddings, trained on paired data from a hybrid relighting pipeline.