Plurato123/OctLLM

Model

Ran Dan1,

4

8 commits

1 linked in READMEs

updated Oct 5, 2026

See the code

README

OctLLM

Octrees as an Explicit 3D Language

Ran Dan1, Si-Tong Wei1, Pengfei Xiong2, Wei Zhang2, Yadong Mu1, Peng-Shuai Wang1,†

1Peking University   2Independent Researcher
†Corresponding author

arXiv: 2610.02388 Code Project Page

Official model weights for Octrees as an Explicit 3D Language.

OctLLM generated meshes, 3D descriptions, and sparse octrees

OctLLM unifies text-to-3D, image-to-3D, and 3D understanding through an explicit 3D language. It represents shapes as Sparse Octree (S-Octree) sequences: occupancy tokens anchored to 3D coordinates and octree depth. This structured representation connects geometry generation and understanding within a single model.

To learn the 3D modality, OctLLM adds trainable branches alongside a frozen Qwen2.5-VL backbone. Mesh tokens and text/image tokens follow separate processing pathways and interact through shared self-attention. Generated octrees are completed by a 3D U-Net and decoded into textured meshes with pretrained TRELLIS decoders. The video below demonstrates generation, 3D understanding, and language interaction.

This repository contains the OctLLM checkpoint and occupancy completion weights. Use the official implementation for installation and inference; its loading code is required for the custom 3D components. More results are available on the project page.

3d
3d-understanding
endpoints_compatible
image-text-to-text
image-to-3d
multimodal
octree
qwen2_5_vl
safetensors
text-generation-inference
text-to-3d
transformers

Plurato123/OctLLM

Model

Ran Dan1,

4

8 commits

1 linked in READMEs

updated Oct 5, 2026

See the code

README

OctLLM

Octrees as an Explicit 3D Language

Ran Dan1, Si-Tong Wei1, Pengfei Xiong2, Wei Zhang2, Yadong Mu1, Peng-Shuai Wang1,†

1Peking University   2Independent Researcher
†Corresponding author

arXiv: 2610.02388 Code Project Page

Official model weights for Octrees as an Explicit 3D Language.

OctLLM generated meshes, 3D descriptions, and sparse octrees

OctLLM unifies text-to-3D, image-to-3D, and 3D understanding through an explicit 3D language. It represents shapes as Sparse Octree (S-Octree) sequences: occupancy tokens anchored to 3D coordinates and octree depth. This structured representation connects geometry generation and understanding within a single model.

To learn the 3D modality, OctLLM adds trainable branches alongside a frozen Qwen2.5-VL backbone. Mesh tokens and text/image tokens follow separate processing pathways and interact through shared self-attention. Generated octrees are completed by a 3D U-Net and decoded into textured meshes with pretrained TRELLIS decoders. The video below demonstrates generation, 3D understanding, and language interaction.

This repository contains the OctLLM checkpoint and occupancy completion weights. Use the official implementation for installation and inference; its loading code is required for the custom 3D components. More results are available on the project page.

3d
3d-understanding
endpoints_compatible
image-text-to-text
image-to-3d
multimodal
octree
qwen2_5_vl
safetensors
text-generation-inference
text-to-3d
transformers