arXiv Computer Vision
MoDGS: Dynamic Gaussian Splatting from Casually-captured Monocular Videos with Depth Priors
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Aug 3
D^2-4DGS: Dual-Depth Guided Sparse-Camera 4D Gaussian Splatting
Dynamic 4D Gaussian Splatting has emerged as an efficient representation for dynamic novel view synthesis through explicit scene modeling and real-time rendering. However, existing methods typically require dense multi-view videos for sufficient geometric constraints, making capture expensive and limiting sparse-camera deployment.
arXiv AI
Jul 2
World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video
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.
arXiv Machine Learning
Jun 25
SparseGS: Sparse View Synthesis using 3D Gaussian Splatting
arXiv:2312. 00206v4 Announce Type: replace-cross Abstract: 3D Gaussian Splatting (3DGS) has recently enabled real-time rendering of unbounded 3D scenes for novel view synthesis.
Hugging Face Trending Papers
Aug 11
CasDeblurGS: Cascaded 2D-to-3D Multi-View Consistency for 3D Gaussian Splatting from Two Blurry Images
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.
arXiv Computer Vision
Aug 25
Towards Alias-Free 4D Gaussian Representations with Motion-Aware Filtering
arXiv:2608.21828v1 Announce Type: new Abstract: Novel-view synthesis of dynamic scenes, crucial for AR/VR applications, remains a challenging problem. Recent methods adapt representations like 3D Gau...
arXiv Computer Vision
Aug 25
GaussVid: Sparse-View Gaussian Splatting with 3D-Aware Video Diffusion Priors
GaussVid introduces a 3D-aware video restoration framework that enhances sparse-view 3D Gaussian Splatting (3DGS) reconstructions. By creating a large-scale 3DGS video dataset and employing a camera-conditioned geometric prior anchored on the first and last frames, the method injects spatial structure into video generation, ensuring geometrically grounded restoration across viewpoints. Experiments demonstrate superior pixel- and structure-level fidelity (PSNR/SSIM) and improved multi-view consistency compared to other video-prior restoration methods, while maintaining competitive perceptual quality (LPIPS).