PhysCoRe

Multi-view RGB-D recordings of deformable objects being manipulated by hand, plus the trained model weights, 3D Gaussian splats and configs needed to reproduce the results in PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics (CoRL 2026).

• Project page: https://lunarlab-gatech.github.io/PhysCoRe-website/
• Paper: https://arxiv.org/abs/2607.20653
• Code: https://github.com/lunarlab-gatech/PhysCoRe

Contents

data/
├── phystwin.zip                 14 cases, redistributed (see Licenses)
└── physcore.zip                 12 cases, recorded by us
gaussian_output/physcore.zip     static 3DGS scenes for the 12 physcore cases
checkpoints/                     MfM_checkpoint.pt, RfD_checkpoint.pt
configs/                         one YAML per pipeline script

The recordings are shipped as archives to keep the repository to a handful of files. Unzip each one in place; every archive expands into a directory of the same name:

cd data            && unzip phystwin.zip && unzip physcore.zip && cd ..
cd gaussian_output && unzip physcore.zip && cd ..

which gives data/phystwin/different_types/<case>/, data/physcore/different_types/<case>/ and gaussian_output/physcore/<case>/.

Each of the 26 cases is one recording from 3 calibrated RGB-D cameras, time-aligned:

path	content
calibrate.pkl	pickled list of 3 camera-to-world 4x4 matrices
metadata.json	per-camera intrinsics, image size WH, frame_num, serial numbers
color/{0,1,2}/<frame>.png	RGB frames
color/{0,1,2}.mp4	per-camera RGB video
depth/{0,1,2}/<frame>.npy	depth frames, uint16 millimeters
mask/{0,1,2}/<mask_id>/<frame>.png	object and controller segmentation
mask/mask_info_{0,1,2}.json	mask id to label mapping
sampled_tracks.pkl	sampled 3D tracks

Where to start

Unzip the three archives as above, then copy the contents of this repository into the root of a PhysCoRe checkout, so that data/, gaussian_output/, checkpoints/ and configs/ sit beside the pipeline scripts. The code repository's README walks through the stages in order and names the config each one reads.

Because mask/ and sampled_tracks.pkl are included, you can skip the segmentation and tracking stage and go straight to building episodes with datagen/convert3d/convert_to_episode.py. Run that earlier stage only if you want to reproduce it; it additionally needs the SAM 2 and GroundingDINO weights, which are not redistributed here.

The configs/ here carry the values used for the released results, with run directories left as YYYYMMDD_hhmm placeholders to be stamped at launch. Per-case controller contact radii in per_sample_rollout_config are scene-dependent and will need retuning for your own objects.

Confidence overlay settings, per case

configs/render_MfM_confidence_3dgs.yaml ships one example case, but the colormap ceiling has to be set per case. These are the values behind the released overlay videos, measured with checkpoints/MfM_checkpoint.pt on data_episodes/physcore/<case>/episode_0000:

case	norm_hi	video_frames	contact radius
double_clift_cloth	2.0	120	0.06 (CLI override)
single_clift_cloth	1.75	null	0.044 (CLI override)
single_clift_rope	2.5	null	0.04
double_squeeze_plastic	2.8	null	0.04
double_stretch_bear_1	15.0	null	0.06
single_push_rope	2.7	150	0.02

The contact radius applies to the validate_MfM.py run that produces render.traj_path, not to the render itself. configs/validate_MfM.yaml already resolves to the value above for every case except the two marked CLI override, which need it passed on the command line:

python validate_MfM.py --config configs/validate_MfM.yaml \
  --root data_episodes/physcore/double_clift_cloth/episode_0000 \
  rollout.manipulation_controller_grid_contact_radius=0.06

Licenses

This repository is mixed-license. Check the directory before reusing anything.

path	license
data/phystwin/	MIT, Copyright (c) 2025 Hanxiao Jiang — see data/phystwin/LICENSE
data/physcore/, gaussian_output/, checkpoints/, configs/	CC-BY-4.0, Lunar Lab @ Georgia Tech

The 14 cases under data/phystwin/ are redistributed from the PhysTwin dataset under its MIT license, which permits redistribution provided the copyright notice is retained. The mask/ and sampled_tracks.pkl files in those case directories are derived from those recordings and carry the same terms. If you use them, please cite PhysTwin as well as this work.

Citation

We hope this dataset is useful for your research. If it contributes to your work, please consider citing:

@inproceedings{yin2026physcore,
  title     = {PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics},
  author    = {Yin, Haocheng and Tao, Shuohan and Chen, Yongsheng and Gan, Lu},
  booktitle = {Conference on Robot Learning (CoRL)},
  series    = {Proceedings of Machine Learning Research},
  publisher = {PMLR},
  year      = {2026}
}