Project Page | Arxiv | Data

Zihan Wang, Jeff Tan, Tarasha Khurana*, Neehar Peri*, Deva Ramanan

Carnegie Mellon University

* Equal Contribution

git clone --recursive https://github.com/MonoFusion/MonoFusion.git
cd MonoFusion
conda create -n monofusion python=3.10
conda activate monofusion
pip install -r requirements.txt
pip install git+https://github.com/nerfstudio-project/gsplat.git
# extra deps for preprocessing
cd preproc && ./setup_dependencies.sh && cd -

• cd preproc/ExoRecon and follow README.md there:

  conda env create -f egorecon.yml
  conda activate egorecon
  python -m pip install -e projectaria_tools_pkg
  ./push_all_data.sh  # downloads + restructures Ego-Exo4D takes

• Each take should end up as MonoFusion/raw_data/<SEQ_NAME>/ containing aria01.vrs, frame_aligned_videos/, trajectory/Dy_train_meta.json, and timestep.txt.

cd preproc
python process_custom.py \
  --img-dirs ../raw_data/<SEQ_NAME>/images \
  --gpus 0 1

• Generates depth, masks, TAPIR tracks, and DUSt3R alignment into ../data/<SEQ_NAME>/.

# edit opt.sh so SEQ_NAME matches _<SEQ_NAME> used during preprocessing
bash opt.sh <experiment_prefix>

• The script appends a timestamp, calls dance_glb.py, logs to ./results_<SEQ_NAME>/<experiment_prefix>_<timestamp>/, and saves checkpoints under checkpoints/ inside that folder.
• Advanced runs can invoke python dance_glb.py --seq_name <SEQ_NAME> --exp <NAME> [Tyro args] directly.

bash vis.sh ./results_<SEQ_NAME>/<RUN_NAME> 7007

• WORK_DIR is the exact path produced in step 4.
• Pick any open TCP port; the script launches run_rendering.py for inspection.

If you find our data, code processing, or project useful, please kindly consider citing our work:

@InProceedings{Wang_2025_ICCV,
    author    = {Wang, Zihan and Tan, Jeff and Khurana, Tarasha and Peri, Neehar and Ramanan, Deva},
    title     = {MonoFusion: Sparse-View 4D Reconstruction via Monocular Fusion},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2025},
    pages     = {8252-8263}
}

Code is built from Shape-of-Motion, thanks for wonderful codebase!