LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models (2411.09595v1)

Abstract: This work explores expanding the capabilities of LLMs pretrained on text to generate 3D meshes within a unified model. This offers key advantages of (1) leveraging spatial knowledge already embedded in LLMs, derived from textual sources like 3D tutorials, and (2) enabling conversational 3D generation and mesh understanding. A primary challenge is effectively tokenizing 3D mesh data into discrete tokens that LLMs can process seamlessly. To address this, we introduce LLaMA-Mesh, a novel approach that represents the vertex coordinates and face definitions of 3D meshes as plain text, allowing direct integration with LLMs without expanding the vocabulary. We construct a supervised fine-tuning (SFT) dataset enabling pretrained LLMs to (1) generate 3D meshes from text prompts, (2) produce interleaved text and 3D mesh outputs as required, and (3) understand and interpret 3D meshes. Our work is the first to demonstrate that LLMs can be fine-tuned to acquire complex spatial knowledge for 3D mesh generation in a text-based format, effectively unifying the 3D and text modalities. LLaMA-Mesh achieves mesh generation quality on par with models trained from scratch while maintaining strong text generation performance.

• The paper presents a unified framework that converts 3D mesh data into text tokens, allowing LLMs to process and generate meshes natively.
• It employs OBJ file format conversion and supervised fine-tuning to teach LLMs the spatial relationships needed for detailed 3D mesh creation.
• This advancement enhances LLM versatility with potential applications in computer graphics, virtual reality, and robotics.

Overview of LLaMA-Mesh: Unifying 3D Mesh Generation with LLMs

The paper "LLaMA-Mesh: Unifying 3D Mesh Generation with LLMs" introduces an innovative approach for generating 3D meshes using LLMs. This approach leverages the pre-existing capabilities of LLMs, which are primarily trained for text processing, to also generate and understand 3D mesh data without necessitating significant alterations to their model architecture or tokenization mechanisms. The primary contribution of this work is the Llama-Mesh framework, which enables LLMs to process 3D mesh data by representing it in a format that is natively readable by these models.

Key Contributions

The paper identifies and addresses a major gap in the integration of LLMs with non-textual data, specifically 3D meshes. LLaMA-Mesh builds on the foundation of LLMs pretrained on text and extends their utility to traversing heterogenous modalities. The principal challenge in this endeavor is the tokenization of 3D mesh data into a format that can be seamlessly processed by LLMs. To overcome this, the authors propose representing mesh data—including vertex coordinates and face definitions—as plain text, effectively converting numerical data into a sequential format that LLMs can natively understand and generate.

Methodology

The authors leverage the OBJ file format, a standard text-based representation for 3D meshes, as the basis for integrating 3D data with LLMs. By treating vertex coordinates and face indices as sequences of text tokens, the Llama-Mesh method enables the model to generate and interpret 3D mesh data directly from language inputs. This transformation facilitates end-to-end training of a unified model capable of generating both text and 3D meshes from interleaved datasets.

A supervised fine-tuning (SFT) approach is employed, where the model is trained on a curated dataset that pairs text prompts with 3D mesh outputs. This SFT process allows the model to learn intricate spatial relationships and develop the ability to generate complex 3D meshes based on textual descriptions. The researchers deployed a version of the LLaMA model for this purpose, demonstrating that it is possible to strengthen an LLM's generative capabilities across multiple modalities without extensive retraining.

Results

The Llama-Mesh framework exhibits mesh generation performance comparable to models that are specifically trained for this task, all the while retaining proficient text generation abilities. This synergy between text and 3D mesh generation represents a compelling advancement in the versatility of LLMs. The results displayed in the paper demonstrate the model's capacity to produce high-quality, artistically complex 3D meshes, showcasing diversity and fidelity in the generated outputs.

Implications and Future Directions

The implications of this research are manifold. Practically, the integration of 3D mesh generation within LLMs could transform workflows in fields such as computer graphics, virtual reality, and robot navigation, offering a robust, language-driven interface for 3D content creation. Theoretically, this approach marks a step toward more holistic AI systems capable of processing and generating multimodal data seamlessly.

Future research could explore the extension of this methodology to incorporate additional 3D features such as textures and dynamic properties, potentially enabling even richer generative applications in virtual environments. Furthermore, enhancing the ability of models to process larger context lengths could increase the complexity and detail of generated meshes. Research could also explore leveraging this method's advancements to integrate other modality types, advancing LLMs towards truly multi-modal generative AI systems.