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.
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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 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
U-4DGS reformulates occluded dynamic human rendering as MAP estimation under heteroscedastic noise, using a Probabilistic Deformation Network and uncertainty-modulated joint rasterization plus confidence-aware regularizations to deliver SOTA fidelity and robustness on ZJU-MoCap and OcMotion.
TrioMan is a tri-module data augmentation framework using a Generator for pose/camera perturbations, a Refiner with one-step diffusion, and an Examiner with dual-branch attention to improve 3D avatar learning from monocular videos, claiming better results than prior methods on two benchmarks.
citing papers explorer
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Ground4D: Spatially-Grounded Feedforward 4D Reconstruction for Unstructured Off-Road Scenes
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.
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Uncertainty-Aware 4D Gaussian Splatting for Monocular Occluded Human Rendering
U-4DGS reformulates occluded dynamic human rendering as MAP estimation under heteroscedastic noise, using a Probabilistic Deformation Network and uncertainty-modulated joint rasterization plus confidence-aware regularizations to deliver SOTA fidelity and robustness on ZJU-MoCap and OcMotion.
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Generator-Refiner-Examiner: A Tri-Module Data Augmentation Framework for 3D Human Avatar Learning from Monocular Videos
TrioMan is a tri-module data augmentation framework using a Generator for pose/camera perturbations, a Refiner with one-step diffusion, and an Examiner with dual-branch attention to improve 3D avatar learning from monocular videos, claiming better results than prior methods on two benchmarks.