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.
GrainGS is a dynamic Gaussian framework that combines a hierarchical anchor scaffold with per-Gaussian deformation that achieves high reconstruction quality, real-time novel view synthesis, and compact storage.
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.· 0 citations
Free-viewpoint 3D scene media is increasingly important for immersive applications, yet practical capture often suffers from severe view sparsity and motion blur. Although neural rendering has advanced sparse-view synthesis, existing blur-aware methods typically require substantial multi-view redundancy, accurate camera poses, or costly per-scene optimization. We address a stringent yet practical setting: reconstructing a coherent 3D scene from only two motion-blurred images with known intrinsics, without input-view poses, auxiliary sharp images, or per-scene test-time optimization. To this end, we propose CasDeblurGS, a cascaded framework that progressively recovers reliable cross-view information from local 2D correspondences to global 3D guidance. Stage 1 constructs locally reliable guidance through occlusion-aware correspondence filtering, while Stage 2 aggregates the intermediate restorations into a provisional pose-free 3D Gaussian representation whose input-view re-renders provide dense global guidance for final restoration. The resulting views enable a more coherent 3D representation and higher-quality novel-view synthesis. Experiments on real-world and synthetic Deblur-NeRF scenes show consistent gains over strong baselines, improving PSNR by 1.19 dB and 2.11 dB, respectively. Progressive ablations, cross-view correspondence visualization, and camera reprojection analysis further demonstrate improvements in both rendering quality and multi-view geometric consistency.
This paper proposes a semantics-guided scene decoupling module that separates Gaussian primitives into static and dynamic components based on motion vectors, and introduces a motion-aware densification module for motion compensation, which alleviates the incomplete rendering of dynamic objects caused by insufficient spatio-temporal information.
Chulin Zhao, Xue Wang, Guoqing Zhou et al.· IEEE Transactions on Visuali...· 0 citations
This work fundamentally reimagines motion blur handling through a paradigm shift: rather than removing blur artifacts, the challenge is reformulate the challenge as a well-constrained forward problem that generatively models blur formation within the rendering pipeline.
DReSG represents attention-guided diffusion proposals as residual targets relative to the current render, and progressively absorbs these residuals into a shared Gaussian scene through multi-view Gaussian feedback.
Zhongliang Liu, Wenjie Liu, Yang Li· 0 citations
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