Uni 3DAR

🚀 Uni-3DAR

Uni-3DAR is an autoregressive model that unifies various 3D tasks, offering improvements in unified data handling, diverse task support, efficiency, and accuracy.

🚀 Quick Start

Introduction

Schematic illustration of the Uni-3DAR framework

Uni-3DAR is an autoregressive model that unifies various 3D tasks. In particular, it offers the following improvements:

1. Unified Handling of Multiple 3D Data Types.
   Although we currently focus on microscopic structures such as molecules, proteins, and crystals, the proposed method can be seamlessly applied to macroscopic 3D structures.

2. Support for Diverse Tasks.
   Uni-3DAR naturally supports a wide range of tasks within a single model, especially for both generation and understanding.

3. High Efficiency.
   It uses octree compression - in combination with our proposed 2 - level subtree compression - to represent the full 3D space using only hundreds of tokens, compared with tens of thousands in a full - size grid. Our inference benchmarks also show that Uni-3DAR is much faster than diffusion - based models.

4. High Accuracy.
   Building on octree compression, Uni-3DAR further tokenizes fine - grained 3D patches to maintain structural details, achieving substantially better generation quality than previous diffusion - based models.

✨ Features

News

2025-03-21: We have released the core model along with the QM9 training and inference pipeline.

📦 Installation

Dependencies

• Uni-Core. For convenience, you can use our prebuilt Docker image:
  docker pull dptechnology/unicore:2407-pytorch2.4.0-cuda12.5-rdma

💻 Usage Examples

Reproducing Results on QM9

To reproduce results on the QM9 dataset using our pretrained model or train from scratch, please follow the instructions below.

Download Pretrained Model and Dataset

Download the pretrained checkpoint (qm9.pt) and the dataset archive (qm9_data.tar.gz) from our Hugging Face repository.

Inference with Pretrained Model

To generate QM9 molecules using the pretrained model:

bash inference_qm9.sh qm9.pt

Train from Scratch

To train the model from scratch:

1. Extract the dataset:

tar -xzvf qm9_data.tar.gz

2. Run the training script with your desired data path and experiment name:

base_dir=/your_folder_to_save/ bash train_qm9.sh ./qm9_data/ name_of_your_exp

📄 License

This project is licensed under the MIT license.

📚 Documentation

Citation

Please kindly cite our papers if you use the data/code/model.

@article{lu2025uni3dar,
  author    = {Shuqi Lu and Haowei Lin and Lin Yao and Zhifeng Gao and Xiaohong Ji and Weinan E and Linfeng Zhang and Guolin Ke},
  title     = {Uni-3DAR: Unified 3D Generation and Understanding via Autoregression on Compressed Spatial Tokens},
  journal   = {Arxiv},
  year      = {2025},
}