MoGaF groups Gaussians by motion in 4D splatting representations to enable stable long-term forecasting of dynamic scenes.
Dynmf: Neural motion factorization for real-time dynamic view synthesis with 3d gaussian splatting
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
SV-GS estimates a time-dependent skeleton pose plus fine deformations to enable 4D Gaussian splatting from sparse views, outperforming prior sparse methods by up to 34% PSNR on synthetic data and matching dense monocular baselines on real data with far fewer frames.
citing papers explorer
-
Space-Time Forecasting of Dynamic Scenes with Motion-aware Gaussian Grouping
MoGaF groups Gaussians by motion in 4D splatting representations to enable stable long-term forecasting of dynamic scenes.
-
SV-GS: Sparse View 4D Reconstruction with Skeleton-Driven Gaussian Splatting
SV-GS estimates a time-dependent skeleton pose plus fine deformations to enable 4D Gaussian splatting from sparse views, outperforming prior sparse methods by up to 34% PSNR on synthetic data and matching dense monocular baselines on real data with far fewer frames.