CG-MLLM: 3D Content Understanding and Generation Driven by Multimodal Large Language Models

CG-MLLM is a research project accepted by ICML 2026, with the core goal of exploring how to use multimodal large language models to achieve automatic captioning and generation of 3D content. This project bridges text, images, and the 3D world, providing a new technical path for the intelligent processing of 3D content.

In the past few years, AI has made significant progress in content generation, such as DALL-E and Midjourney for text-to-image generation, and Sora for text-to-video generation. However, 3D content understanding and generation are more challenging: 3D data contains multi-dimensional information such as appearance (texture, color), geometric structure, spatial relationships, and physical properties. Enabling AI to truly "understand" the 3D world and perform description/generation is an important direction in computer vision and graphics. CG-MLLM is a solution proposed to address this challenge.

Multimodal Large Language Models (MLLM) gain the ability to process visual content by introducing visual encoders. Their typical architecture includes three core components:

1. Visual Encoder: Converts images/videos into feature representations (e.g., CLIP visual encoder, ViT);
2. Projection Layer: Maps visual features to the input space of the language model;
3. Large Language Model Backbone: Integrates visual and text information based on Transformer for multimodal reasoning and generation. This architecture proves that the abstract reasoning ability of language models can be transferred to visual tasks, enabling multimodal understanding.

CG-MLLM proposes a systematic solution to address challenges in the 3D domain:

Unified 3D Representation Learning

It may use 3D-aware encoders (e.g., Point Transformer) to directly extract features from raw 3D data, or fuse information after rendering multi-view 2D images.

3D-Language Alignment Strategy

Including contrastive learning (narrowing the distance between matched 3D and text features), generative pre-training (generating text from 3D or vice versa), and instruction fine-tuning (performing 3D understanding tasks).

Dual-Task Learning Framework

"CG" stands for Captioning (description generation: generating natural language descriptions from 3D) and Generating (content generation: generating 3D content from text). The two tasks are trained jointly to promote each other.

Once CG-MLLM technology matures, it will unlock multiple application scenarios:

• Democratization of 3D Content Creation: Lower the threshold for 3D modeling, allowing ordinary users to generate 3D assets via text;
• Intelligent 3D Asset Retrieval: Semantic-based natural language retrieval of 3D model libraries;
• VR/AR: Provide support for dynamic content generation in virtual worlds;
• Robotics and Autonomous Driving: Natural language interfaces facilitate human-machine interaction;
• 3D Content Accessibility: Generate voice descriptions for visually impaired users or create 3D content from voice.

The field of 3D multimodal learning still faces challenges:

• Balance Between Generation Quality and Efficiency: Need to find a balance between high-quality generation and computational efficiency;
• Fine-Grained Control Ability: Improve the editing and control of details in generated content;
• Physical Consistency: Introduce physical constraints to ensure generated content complies with laws of physics;
• Multimodal Fusion: Deeply integrate 3D with text, image, audio, and other modalities to build a universal multimodal AI system.

CG-MLLM is an important step for AI to advance into the 3D world, expanding the capabilities of multimodal large language models in the 3D domain. In the future, creating 3D content may become as simple as writing text, profoundly changing the creative methods in industries such as games, film and television, and design. This project provides an excellent starting point for researchers to explore 3D multimodal learning, and it is worth in-depth research and innovation.