CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models

• URL: http://arxiv.org/abs/2601.21798v1
• Date: Thu, 29 Jan 2026 14:42:46 GMT
• Title: CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models
• Authors: Junming Huang, Weiwei Xu,
• Abstract summary: CG-MLLM is a novel Large Language Model capable of 3D captioning and high-resolution 3D generation in a single framework.<n>By integrating a pre-trained vision-language backbone with a specialized 3D VAE latent space, CG-MLLM facilitates long-context interactions between standard tokens and spatial blocks.
• Score: 18.035268191933117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models(LLMs) have revolutionized text generation and multimodal perception, but their capabilities in 3D content generation remain underexplored. Existing methods compromise by producing either low-resolution meshes or coarse structural proxies, failing to capture fine-grained geometry natively. In this paper, we propose CG-MLLM, a novel Multi-modal Large Language Model (MLLM) capable of 3D captioning and high-resolution 3D generation in a single framework. Leveraging the Mixture-of-Transformer architecture, CG-MLLM decouples disparate modeling needs, where the Token-level Autoregressive (TokenAR) Transformer handles token-level content, and the Block-level Autoregressive (BlockAR) Transformer handles block-level content. By integrating a pre-trained vision-language backbone with a specialized 3D VAE latent space, CG-MLLM facilitates long-context interactions between standard tokens and spatial blocks within a single integrated architecture. Experimental results show that CG-MLLM significantly outperforms existing MLLMs in generating high-fidelity 3D objects, effectively bringing high-resolution 3D content creation into the mainstream LLM paradigm.

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