REVIEW 7 cited by
Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene Reconstruction
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
3D Gaussian Splatting (3DGS) has become an emerging tool for dynamic scene reconstruction. However, existing methods focus mainly on extending static 3DGS into a time-variant representation, while overlooking the rich motion information carried by 2D observations, thus suffering from performance degradation and model redundancy. To address the above problem, we propose a novel motion-aware enhancement framework for dynamic scene reconstruction, which mines useful motion cues from optical flow to improve different paradigms of dynamic 3DGS. Specifically, we first establish a correspondence between 3D Gaussian movements and pixel-level flow. Then a novel flow augmentation method is introduced with additional insights into uncertainty and loss collaboration. Moreover, for the prevalent deformation-based paradigm that presents a harder optimization problem, a transient-aware deformation auxiliary module is proposed. We conduct extensive experiments on both multi-view and monocular scenes to verify the merits of our work. Compared with the baselines, our method shows significant superiority in both rendering quality and efficiency.
Forward citations
Cited by 7 Pith papers
-
4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans
A diffusion model trained on 60,000 fitted 4D Gaussian Splatting human clips generates text-prompted, view-consistent dynamic humans directly in 4D, over 10x faster than video-first pipelines.
-
VoxelSplat: Dynamic Gaussian Splatting as an Effective Loss for Occupancy and Flow Prediction
A training-only Gaussian splatting loss, which renders predicted 3D semantics and motion into 2D camera views, improves semantic occupancy and scene flow prediction across several camera-based models.
-
FreeTimeGS: Free Gaussian Primitives at Anytime and Anywhere for Dynamic Scene Reconstruction
A dynamic-scene representation where Gaussian primitives live freely in 4D space-time with linear motion and Gaussian time windows achieves state-of-the-art novel-view quality on complex-motion benchmarks.
-
3D Gaussian Representations with Motion Trajectory Field for Dynamic Scene Reconstruction
A 3D Gaussian Splatting model whose Gaussian centers are represented as a learned combination of shared global motion bases recovers dynamic scenes and motion trajectories from monocular video.
-
Enhanced Velocity Field Modeling for Gaussian Video Reconstruction
Velocity field rendering with flow-based losses and flow-assisted densification lifts dynamic Gaussian novel-view PSNR by about 2.5 dB on Nvidia-long and Neu3D.
-
PCR-GS: COLMAP-Free 3D Gaussian Splatting via Pose Co-Regularizations
PCR-GS stabilizes pose-free 3D Gaussian Splatting on fast-moving video by aligning DINO semantic features and wavelet high-frequency details between neighboring frames.
-
SkinningGS: Editable Dynamic Human Scene Reconstruction Using Gaussian Splatting Based on a Skinning Model
A UV-texture-driven Gaussian splatting avatar method claims faster, leaner, and better human-scene reconstruction than HUGS, but its tables contain internal inconsistencies.
Discussion (0). Continue with ORCID to comment.