Papers With Code
metadata only
GAE: Learning a Geometry-Native Latent Space for 3D-Consistent World Generation
We present a compact geometry-native latent space as a shared foundation for perception and generation. Visual generators can produce photorealistic frames without preserving a consistent 3D scene. We argue that this is not only a modeling problem but also a representation problem: generators typica
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- GAE: Learning a Geometry-Native Latent Space for 3D-Consistent World Generation#1051373
- Native and Compact Structured Latents for 3D Generation#400144
- iARCS: Iterative Agentic RL for Controllable 3D Scene Generation#432576
- Code-as-Room: Generating 3D Rooms from Top-Down View Images via Agentic Code Synthesis#203135
- 3D-LLM: Injecting the 3D World into Large Language Models#16619
Record · ID 1067528
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive