The paper introduces an affine‑aligned atlas for constructing canonical Gaussian representations in video. By applying frame‑wise affine transforms to absorb global motion before building the canonical Gaussians, the method reduces misalignment between the canonical model and individual frames. This modification can be added to existing Gaussian‑based video representations with minimal extra parameters and improves reconstruction quality, particularly for sequences with large camera motion.
arXiv:2512.07197v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful explicit representation enabling real-time, high-fidelity 3D reconstruction and novel view s...
arXiv:2608.30184v1 Announce Type: new Abstract: Volumetric video enables immersive free viewpoint rendering of dynamic real world scenes, yet existing methods struggle with long sequences and complex...
arXiv:2607. 01202v1 Announce Type: cross Abstract: We present World from Motion, a method for generating freely renderable dynamic 3D Gaussian representations from monocular videos.
GeoBlur is a framework that estimates the fundamental matrix and relative camera pose from a single motion‑blurred image by exploiting blur artifacts as motion cues. It predicts visual correspondences between two time instances within the exposure window and solves the single‑frame epipolar geometry problem, yielding a fundamental matrix unique up to transposition due to time‑direction ambiguity. The method shows improved performance on synthetic and hybrid benchmarks and remains competitive on real motion‑blur data, also enabling downstream single‑frame motion segmentation.
OC-GS introduces an object‑centric Gaussian splatting method that refines the angle of each image while keeping a shared camera, rotation axis, and pivot for turntable reconstruction. By jointly optimizing image‑derived geometry and angles, it reconstructs objects from sparse, irregular captures and outperforms four pose‑free Gaussian splatting baselines across various view counts. Ablation studies confirm that both image‑derived angle initialization and the shared motion model are essential for the observed improvements, with real captures showing a 0.70 dB PSNR gain.
VGGT-GS SLAM is a monocular 3D Gaussian Splatting SLAM system that operates on uncalibrated videos. It uses feed‑forward VGGT pose and depth priors to perform submap differentiable bundle adjustment, jointly refining camera poses, a 3D Gaussian map, and submap‑shared intrinsics and distortion parameters via analytic calibration Jacobians. The method introduces Gaussian‑native alignment for camera‑anchored scale refinement between submaps and loop‑closure verification, achieving improved localization accuracy and rendering quality on standard indoor benchmarks.
arXiv:2605. 03337v3 Announce Type: replace-cross Abstract: Recent progress in 4D Gaussian Splatting (4DGS) has achieved impressive dynamic scene reconstruction results.
arXiv:2608. 01958v2 Announce Type: replace-cross Abstract: 4D Gaussian Splatting (4DGS) excels in dynamic 3D reconstruction and real-time novel view synthesis via efficient 4D Gaussian representations and parallelizable rendering.
VoxelTTO is a feed‑forward framework that reconstructs 3D Gaussian splatting scenes from multiple images by aggregating dense image features into a global voxel representation and decoding Gaussians from voxel features, thereby eliminating the pixel‑to‑Gaussian correspondence. It incorporates test‑time optimization with lightweight LoRA modules to adapt to known camera parameters while keeping the pretrained visual foundation model frozen. The method replaces standard rasterization with stochastic solid volume rendering, improving geometric fidelity, and demonstrates superior RGB‑D novel‑view synthesis and camera‑pose estimation on Replica, Tanks and Temples, and DTU datasets.
Forge4D is a feed‑forward model that reconstructs temporally aligned 4D human representations from uncalibrated sparse‑view videos, enabling both novel view and novel time synthesis. It achieves this by jointly streaming 3D Gaussian reconstruction with dense motion prediction, using learnable state tokens for temporal consistency and a self‑supervised retargeting loss for motion prediction. Extensive experiments confirm its effectiveness on in‑domain and out‑of‑domain datasets.
We study dynamic Gaussian Splatting from monocular videos. While recent advancements in dynamic Gaussian splatting offer a promising foundation for modeling dynamic scenes, they often overfit to the t...
arXiv:2609.00994v1 Announce Type: new Abstract: Recent extensions of 3D Gaussian Splatting (3DGS) enable real-time novel view synthesis in dynamic scenes by learning time-conditioned Gaussian deforma...