Jul 2026· IEEE Transactions on Visualization and Computer Graphics· Vol PP, pp. 1-15· 0 citations
Medicine
TL;DR
This work introduces a novel prior-guided, multi-stage reconstruction pipeline that fuses geometric and motion priors to build a physically plausible and temporally stable foundation, and applies a universal trajectory-preserving technique, which safeguards high-quality motion by decoupling the appearance optimization from the learned deformation.
Abstract
Editing dynamic scenes with 4D Gaussian Splatting (4DGS) is often hampered by spatiotemporal inconsistencies, or "Gaussian drifting", which degrades edit quality and temporal coherence. We identify that these artifacts stem from two distinct sources: foundational inaccuracies in the initial scene reconstruction, and the disruption of learned trajectories during the editing process itself. To address this, we propose a comprehensive framework that systematically tackles both sources of inconsistency. To solve reconstruction-induced errors, we introduce a novel prior-guided, multi-stage reconstruction pipeline that fuses geometric and motion priors to build a physically plausible and temporally stable foundation. To solve editing-induced errors, we further apply a universal trajectory-preserving technique, which safeguards high-quality motion by decoupling the appearance optimization from the learned deformation. Experiments demonstrate that by systematically addressing both the reconstruction and editing phases, our method achieves state-of-the-art, temporally consistent editing on a wide range of dynamic scenes where previous monolithic approaches fail.
Sparse or limited observations often cause 3D Gaussian Splatting (3DGS) scenes to exhibit holes, missing content, and unstable appearance in novel views. Repairing these regions requires generating synthetic frames that remain consistent across viewpoints, a challenge closely related to cross-view drift in generative m...
Max Eskandari, Xing-Bang Tang, Matthew J. Kyan· Conference on Multimedia Inf...· 0 citations
This work proposes a novel 3D-aware video restoration framework designed to enhance the quality of sparse 3DGS reconstruction and introduces a camera-conditioned geometric prior that guides the network toward geometrically grounded restoration that remains coherent across viewpoints.
Xinhui Liu, Can Wang, Wei Jiang et al.· 1 citation
This paper proposes a method to address gradient vanishing with a piecewise truncated gradient formulation that improves the optimization stability and robustness to initializations and introduces a novel dataset for benchmarking dynamic Gaussian Splatting using synthetic 3D scenes.
Théo Morales, Nhat-Quynh Le-Pham, Robin Atkins et al.· 0 citations
3D Gaussian Splatting has enabled photorealistic dense mapping for visual Simultaneous Localization and Mapping (SLAM). Nevertheless, most existing pipelines are designed for static scenes and thus degrade under dynamic occlusions, leading to unstable tracking and noticeable mapping artifacts. Prior dynamic solutions o...
Jia-Shun Guo, Xiao-Le Chen, Wen-Di Kang et al.· IEEE Robotics and Automation...· 0 citations
This paper proposes a novel iterative refinement framework based on a video diffusion model to improve the completeness and consistency of dynamic 4D scenes, and substantially outperforms existing baselines.
Hai-Tao Huang, Sheng-Hao Zhao, Bo-Yuan Tian et al.· 0 citations
TaylorMoDe-GS, the first 3DGS framework tailored for multi-view dynamic object deblurring, shifts the modeling paradigm from displacement fitting to velocity driven modeling, and introduces a neural Peano remainder network to compensate for high frequency non-linear dynamics.
Xiao-Feng Quan, Jun-Zhe Wan, Chao Cai et al.· Proceedings of the Thirty-Fi...· 0 citations
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