GeoVerse: World-Consistent Novel View Synthesis in Geometric Latent Space

arXiv:2609.35734 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-29

Project page: https://geoverse-nvs.github.io

Terminology

• Consistency — 21× in this paper · explained in 3 episodes
• Fidelity — 11× in this paper · explained in 2 episodes
• Latent Space — 11× in this paper · explained in 7 episodes
• Novel View Synthesis — 10× in this paper
• Diffusion Model — 9× in this paper · explained in 8 episodes
• Distillation — 9× in this paper · explained in 2 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Foundation Model — 7× in this paper · explained in 3 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Foundation Models — 5× in this paper · explained in 7 episodes
• 3D Gaussian Splatting — 4× in this paper · explained in 2 episodes
• Gaussian Splatting — 4× in this paper · explained in 4 episodes
• World Model — 4× in this paper · explained in 7 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 3× in this paper
• Decode — 3× in this paper · explained in 2 episodes
• Digital Twin — 3× in this paper · explained in 5 episodes
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Flow Matching — 3× in this paper · explained in 8 episodes
• Generative AI — 3× in this paper · explained in 2 episodes
• ACM Transactions on Graphics — 2× in this paper
• Adaptation — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Embeddings — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Optical Flow — 2× in this paper · explained in 2 episodes
• 3D Gaussian Splatting (3DGS) — 1× in this paper · explained in 4 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Geometry Latent Diffusion — 1× in this paper
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes

Sources

• ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data
• Virtual KITTI 2
• Repurposing Geometric Foundation Models for Multi-view Diffusion
• LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias
• CAMEO: Correspondence-Attention Alignment for Multi-View Diffusion Models
• Depth Anything 3: Recovering the Visual Space from Any Views
• WorldMirror: Universal 3D World Reconstruction with Any-Prior Prompting
• Decoupled Weight Decay Regularization
• Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians
• Lyra 2.0: Explorable Generative 3D Worlds
• Wan: Open and Advanced Large-Scale Video Generative Models
• Latent Spatial Memory for Video World Models
• $\pi^3$: Permutation-Equivariant Visual Geometry Learning
• Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
• NeoVerse: Enhancing 4D World Model with in-the-wild Monocular Videos
• ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis
• Fast-WAM: Do World Action Models Need Test-time Future Imagination?
• Stereo Magnification: Learning View Synthesis using Multiplane Images
• OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling

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