OuroWorld: Bringing Any 3D World Alive as Diverse, Endlessly Looping 3D Cinemagraphs

arXiv:2610.12461 · cs.CV, cs.GR · Submitted 2026-10-08 · Read on arXiv

cs.CV, cs.GR

Submitted: 2026-10-08

Updated: 2026-10-08

Project page: https://ouroworld.userwei.com

Terminology

• Conference on Computer Vision and Pattern Recognition — 21× in this paper
• Gaussian Splatting — 19× in this paper · explained in 4 episodes
• Consistency — 15× in this paper · explained in 3 episodes
• Diffusion Model — 10× in this paper · explained in 8 episodes
• Grounding — 9× in this paper · explained in 3 episodes
• 3D Gaussian Splatting — 8× in this paper · explained in 2 episodes
• Diffusion Models — 8× in this paper · explained in 6 episodes
• World Model — 8× in this paper · explained in 7 episodes
• CVF International Conference on Computer Vision — 6× in this paper
• Ablation — 5× in this paper · explained in 1 episode
• Fidelity — 5× in this paper · explained in 2 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Illumination Variation — 4× in this paper
• Motion Variation — 4× in this paper
• Distillation — 3× in this paper · explained in 2 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• 3D Gaussian Splatting (3DGS) — 2× in this paper · explained in 4 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Motion-Aware Loop Fidelity — 2× in this paper
• Transactions on Graphics — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Visual Variation — 2× in this paper
• Ablation study — 1× in this paper · explained in 1 episode
• Decode — 1× in this paper · explained in 2 episodes
• Digital Twin — 1× in this paper · explained in 5 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes

Sources

• Demystifying MMD GANs
• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• AdaGaR: Adaptive Gabor Representation for Dynamic Scene Reconstruction
• Pantheon360: Taming Digital Twin Generation via 3D-Aware 360{\deg} Video Diffusion
• AniGS: Bridging Rendering and Diffusion Prior for 3D Scene Animation
• LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes
• Animating Landscape: Self-Supervised Learning of Decoupled Motion and Appearance for Single-Image Video Synthesis
• World Reconstruction From Inconsistent Views
• ViPE: Video Pose Engine for 3D Geometric Perception
• HY-World 2.0: A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• WildGaussians: 3D Gaussian Splatting in the Wild
• VividDream: Generating 3D Scene with Ambient Dynamics
• Depth Anything 3: Recovering the Visual Space from Any Views
• Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion
• DreamLoop: Controllable Cinemagraph Generation from a Single Photograph
• SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
• Diff4Splat: Controllable 4D Scene Generation with Latent Dynamic Reconstruction Models
• HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields

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