# MeshLLM: Empowering Large Language Models to Progressively Understand and Generate 3D Mesh

arXiv: https://arxiv.org/abs/2508.01242 · HTML: https://mlsanity.com/paper/2508.01242/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 62 of its 74 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Builds on (most cited first, 30 of 62)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://mlsanity.com/paper/2405.04434/) (2024)
- [2D Gaussian Splatting for Geometrically Accurate Radiance Fields](https://mlsanity.com/paper/2403.17888/) (2024)
- [Enhancing Chat Language Models by Scaling High-quality Instructional Conversations](https://mlsanity.com/paper/2305.14233/) (2023)
- [Objaverse-XL: A Universe of 10M+ 3D Objects](https://mlsanity.com/paper/2307.05663/) (2023)
- [DeepSeek LLM: Scaling Open-Source Language Models with Longtermism](https://mlsanity.com/paper/2401.02954/) (2024)
- [SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities](https://mlsanity.com/paper/2305.11000/) (2023)
- [Shap-E: Generating Conditional 3D Implicit Functions](https://mlsanity.com/paper/2305.02463/) (2023)
- [AudioCLIP: Extending CLIP to Image, Text and Audio](https://mlsanity.com/paper/2106.13043/) (2021)

## Built on by (most cited first, 20 of 20)

- [PhysX-Anything: Simulation-Ready Physical 3D Assets from Single Image](https://mlsanity.com/paper/2511.13648/) (2025)
- [PhysX-Omni: Unified Simulation-Ready Physical 3D Generation for Rigid, Deformable, and Articulated Objects](https://mlsanity.com/paper/2605.21572/) (2026)
- [FACE: A Face-based Autoregressive Representation for High-Fidelity and Efficient Mesh Generation](https://mlsanity.com/paper/2603.01515/) (2026)
- [SIMART: Decomposing Monolithic Meshes into Sim-ready Articulated Assets via MLLM](https://mlsanity.com/paper/2603.23386/) (2026)
- [Native 3D Editing with Full Attention](https://mlsanity.com/paper/2511.17501/) (2025)
- [MeshFlow: Mesh Generation with Equivariant Flow Matching](https://mlsanity.com/paper/2606.23489/) (2026)
- [CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models](https://mlsanity.com/paper/2601.21798/) (2026)
- [Know3D: Prompting 3D Generation with Knowledge from Vision-Language Models](https://mlsanity.com/paper/2603.22782/) (2026)
- [Investigating Anthropometric Fidelity in SAM 3D Body](https://mlsanity.com/paper/2601.06035/) (2025)
- [AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer](https://mlsanity.com/paper/2602.12100/) (2026)
- [4DP-QA: Scalable QA for 4D Perception in Vision Language Models](https://mlsanity.com/paper/2606.11568/) (2026)
- [PhysX-CoT: Structured Physical Reasoning from a Single Image to Simulation-Ready 3D Assets](https://mlsanity.com/paper/2608.08053/) (2026)
- [Chat-Edit-3D++: Interactive 3D and 4D Scene Editing via Large Language Models](https://mlsanity.com/paper/2608.29137/) (2026)
- [Beyond Spatial Compression: Interface-Centric Generative States for Open-World 3D Structure](https://mlsanity.com/paper/2605.10438/) (2026)
- [TOPOS: High-Fidelity and Efficient Industry-Grade 3D Head Generation](https://mlsanity.com/paper/2605.14594/) (2026)
- [GReFEM: Multimodal LLMs as Zero-Shot Semantic Assistants for Physics-Guided 3D Mesh Refinement](https://mlsanity.com/paper/2607.08798/) (2026)
- [What Makes a 3D Scene Editable? A Factorized Benchmark of Fidelity, Locality, Consistency, and Preservation](https://mlsanity.com/paper/2609.14899/) (2026)
- [SPOON: Towards Coherent Compositional 3D Scene Generation from Uncalibrated Multi-view Images](https://mlsanity.com/paper/2609.39590/) (2026)
- [Octrees as an Explicit 3D Language](https://mlsanity.com/paper/2610.02388/) (2026)
- [AutoDP-LLM: Automating Data Pre-processing for Intrusion Detection Systems using Large Language Models](https://mlsanity.com/paper/2610.05369/) (2026)

## Abstract (arXiv)

We present MeshLLM, a novel framework that leverages large language models (LLMs) to understand and generate text-serialized 3D meshes. Our approach addresses key limitations in existing methods, including the limited dataset scale when catering to LLMs' token length and the loss of 3D structural information during mesh serialization. We introduce a Primitive-Mesh decomposition strategy, which divides 3D meshes into structurally meaningful subunits. This enables the creation of a large-scale dataset with 1500k+ samples, almost 50 times larger than previous methods, which aligns better with the LLM scaling law principles. Furthermore, we propose inferring face connectivity from vertices and local mesh assembly training strategies, significantly enhancing the LLMs' ability to capture mesh topology and spatial structures. Experiments show that MeshLLM outperforms the state-of-the-art LLaMA-Mesh in both mesh generation quality and shape understanding, highlighting its great potential in processing text-serialized 3D meshes.