Achieving higher reconstruction quality and more accurate camera pose estimation using thousands of frames input.

• [Jan 6 , 2026] Paper release.
• [Jan 6 , 2026] Code release.

• Welcome to check out our previous collaborative work FastVGGT.

We propose InfiniteVGGT, a causal visual geometry transformer that utilizes a training-free rolling memory mechanism to enable stable, infinite-horizon streaming, and introduce the Long3D benchmark to rigorously evaluate long-term continuous 3D geometry performance. Our main contributions are summarized as follows:

1. An unbounded memory architecture InfiniteVGGT for continuous 3D geometry understanding, built on a novel, dynamic, and interpretable explicit memory system.
2. State-of-the-art performance on long-sequence benchmarks and a unique capability for robust, infinite-horizon reconstruction without memory overflow.
3. The Long3D benchmark, a new dataset for the rigorous evaluation of long-term performance, addressing a critical gap in the field.

1. Clone InfiniteVGGT

git clone https://github.com/AutoLab-SAI-SJTU/InfiniteVGGT.git
cd InfiniteVGGT

2. Create conda environment

conda create -n infinitevggt python=3.11 cmake=3.14.0
conda activate infinitevggt 

3. Install requirements

pip install -r requirements.txt
conda install 'llvm-openmp<16'

4. Download the StreamVGGT pretrained checkpoint and place it to ./ckpt directory.

# Run on your own data
python run_inference.py --input_dir path/to/your/images_dir

# Run long sequence and store the result to directory for each frame
python run_inference.py \
    --input_dir path/to/your/images_dir \
    --frame_cache_dir path/to/your/results_perframe_dir \
    --no_cache_results

We provide demo code based on the NRGBD dataset. You can run it using the following command:

python demo_viser.py  \
    --seq_path path/to/nrgbd/image_sequence \
    --frame_interval 10 \
    --gt_path path/to/nrgbd/gt_camera \ (Optional)

• Release the Dataset.

We would like to acknowledge the following open-source projects that served as a foundation for our implementation:

DUSt3R CUT3R VGGT Point3R StreamVGGT FastVGGT TTT3R

Many thanks to these authors!

If you incorporate our work into your research, please cite:

@misc{yuan2026infinitevggt,
        title={InfiniteVGGT: Visual Geometry Grounded Transformer for Endless Streams}, 
        author={Shuai Yuan and Yantai Yang and Xiaotian Yang and Xupeng Zhang and Zhonghao Zhao and Lingming Zhang and Zhipeng Zhang},
        journal={arXiv preprint arXiv:2601.02281},
        year={2026}
}