GAE: Learning a Geometry-Native Latent Space for 3D-Consistent World Generation

arXiv:2609.24981 · cs.CV · Submitted 2026-09-21 · Read on arXiv

cs.CV

Submitted: 2026-09-21

Updated: 2026-09-25

Terminology

• Consistency — 19× in this paper · explained in 3 episodes
• Transformer — 17× in this paper · explained in 2 episodes
• Alignment — 10× in this paper · explained in 1 episode
• Latent Space — 10× in this paper · explained in 7 episodes
• Flow Matching — 8× in this paper · explained in 8 episodes
• Foundation Model — 8× in this paper · explained in 3 episodes
• Decode — 7× in this paper · explained in 2 episodes
• Fidelity — 7× in this paper · explained in 2 episodes
• Foundation Models — 6× in this paper · explained in 7 episodes
• Latent Representation — 6× in this paper · explained in 2 episodes
• Diffusion Model — 5× in this paper · explained in 8 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Point Clouds — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Trace — 2× in this paper · explained in 2 episodes
• World Model — 2× in this paper · explained in 7 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Anisotropy — 1× in this paper · explained in 3 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• International Conference on Computer Vision — 1× in this paper
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Classifier-Free Diffusion Guidance
• Flow Matching in Feature Space for Stochastic World Modeling
• DINOv3
• Improved Baselines with Representation Autoencoders
• LDM3D: Latent Diffusion Model for 3D
• Wan: Open and Advanced Large-Scale Video Generative Models
• Latent Riemannian Flow Matching for Geometry-Grounded 3D Foundation Models
• VideoWeave: Unlocking Geometric Consistency in Video Generation via Joint Geometry-Video Modeling
• Qwen3 Technical Report
• SCoPE: Sightline-Coordinate Positional Encoding for Video Diffusion Transformers
• UFM: A Simple Path towards Unified Dense Correspondence with Flow
• Diffusing in the Right Space: A Systematic Study of Latent Diffusability
• OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling

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