JreamH/CGMLLM-4B-i2o

CG-MLLM 4B image-to-3D

CG-MLLM: Captioning and Generating 3D Content via Multi-modal Large Language Models (ICML 2026)

CG-MLLM is a 3D multimodal large language model built on a Qwen3-VL backbone and a Hunyuan3D-2.1 VAE.

This repository is the 4B image-to-3D checkpoint. It is a specialized training of that architecture for image-to-3D on HY3D-Bench, on Qwen3-VL-4B-Instruct. It is not the multi-task checkpoint.

The v0.1 multi-task checkpoint (Qwen3-VL-2B) is at JreamH/CGMLLM.

Links

• Code (how to run): https://github.com/dreaming-huang/CG-MLLM
• Paper (arXiv): https://arxiv.org/abs/2601.21798
• Hugging Face Papers: https://huggingface.co/papers/2601.21798
• Project page: https://cv.jream.top/CG-MLLM-page/
• ICML poster: https://icml.cc/virtual/2026/poster/63909

Model Details

Item	Value
Version	4B image-to-3D
Vision-language backbone	Qwen3-VL-4B-Instruct
3D latent tokenizer	Hunyuan3D-2.1 VAE
Training	Specialized image-to-3D training on HY3D-Bench
Tasks	Image-to-3D
Checkpoint file	ema.safetensors
Venue	ICML 2026
License	Apache-2.0
Authors	Junming Huang, Chi Wang, Letian Li, Guangkai Xu, Donglin Huang, Hao Chen, Qiang Dai, Weiwei Xu
Affiliations	Zhejiang University; LIGHTSPEED

Usage is documented in the GitHub repository. This card does not duplicate the inference commands.

Citation

If you find this work useful, please cite:

@article{huang2026cg,
  title={CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models},
  author={Huang, Junming and Wang, Chi and Li, Letian and Xu, Guangkai and Huang, Donglin and Chen, Hao and Dai, Qiang and Xu, Weiwei},
  journal={arXiv preprint arXiv:2601.21798},
  year={2026}
}

Acknowledgments

This work builds on Qwen3-VL, BAGEL, and the Hunyuan3D-2.1 shape VAE. Please follow their licenses when using those components.