CoRe3D: Collaborative Reasoning as a Foundation for 3D Intelligence
Tianjiao Yu, Xinzhuo Li, Yifan Shen, Yuanzhe Liu, Ismini Lourentzou
2025-12-16
Summary
This paper introduces CoRe3D, a new system for creating 3D models from text descriptions. It focuses on making the process more logical and understandable, similar to how recent AI models handle language and images, but applies it to the more complex world of 3D shapes.
What's the problem?
Current AI models struggle to reliably create accurate 3D models based on text. They often lack a clear way to 'think' through the process, leading to inconsistencies and models that don't quite match the description. Existing reasoning methods work well for things like text and pictures, but haven't been effectively adapted for the complexities of 3D space.
What's the solution?
CoRe3D solves this by creating a system that breaks down 3D space into smaller, manageable regions. It then uses a 'chain-of-thought' process, similar to how humans reason, to connect the text description to specific parts of the 3D model. This means the AI doesn't just create a shape randomly, but builds it up logically, step-by-step, based on what the text says. It links what things *mean* to where they are *placed* in 3D space.
Why it matters?
This work is important because it makes 3D model generation more reliable and predictable. By making the AI's reasoning process clearer, it's easier to understand *why* a model was created a certain way and to fix any errors. This could have big implications for fields like game development, design, and robotics, where accurate 3D models are essential.
Abstract
Recent advances in large multimodal models suggest that explicit reasoning mechanisms play a critical role in improving model reliability, interpretability, and cross-modal alignment. While such reasoning-centric approaches have been proven effective in language and vision tasks, their extension to 3D remains underdeveloped. CoRe3D introduces a unified 3D understanding and generation reasoning framework that jointly operates over semantic and spatial abstractions, enabling high-level intent inferred from language to directly guide low-level 3D content formation. Central to this design is a spatially grounded reasoning representation that decomposes 3D latent space into localized regions, allowing the model to reason over geometry in a compositional and procedural manner. By tightly coupling semantic chain-of-thought inference with structured spatial reasoning, CoRe3D produces 3D outputs that exhibit strong local consistency and faithful alignment with linguistic descriptions.