KnockGS

Interaction-Grounded Calibration of Physical Gaussian Representations

Chenchen Ge^*, Hanwen Shen^*, Bowen Jing, Jiyuan Cai, Xiaofeng Wang,
Hongsen Lei, Weitao Zhou, Dandan Zhang, Haibao Yu^†

^*Equal contribution    ^†Corresponding author

arXiv | Paper

KnockGS calibrates the effective elasticity and density scales of a physics-integrated 3D Gaussian asset from its response to a known interaction. The estimated scales are frozen, written back into the same simulator, and evaluated by predicting the response to a different, held-out interaction.

Highlights

• Interaction-grounded calibration. KnockGS turns the response to a known Probe A into physical evidence for estimating elasticity and density scales.
• Simple response-space estimator. A shared deterministic descriptor, candidate-only standardization, hard top-k retrieval, and local ridge regression produce continuous material estimates without MPM gradients.
• Frozen cross-interaction prediction. The estimate is frozen before Probe B and evaluated on interactions that differ in direction, magnitude, or both.
• Object-specific but reusable. The Probe-A response library is built once for a fixed Gaussian asset and simulator contract, then reused across target calibrations.

The candidate-library size is denoted by J and is not fixed by the method. The main paper benchmark uses J = 54, while the supplementary study also evaluates smaller libraries.

Results

Across five held-out Pillow targets, local ridge reduces mean joint scale error to 1.13%, compared with 2.37% for response KNN and 2.45% for global ridge under the same Probe-A evidence. The frozen estimate also yields the lowest trajectory error under the held-out direction- and magnitude-shifted probes.

The rendered comparison follows the same ordering. On the illustrated unseen Probe-B sequence, KnockGS reaches 41.2 dB PSNR and 0.998 SSIM, with the smallest final-frame absolute error.

The same object-specific calibration procedure is evaluated on three geometrically distinct Gaussian assets: Pillow, Ficus, and Vasedeck.

Citation

If you find KnockGS useful, please cite:

@article{ge2026knockgs,
  title         = {KnockGS: Interaction-Grounded Calibration of Physical Gaussian Representations},
  author        = {Ge, Chenchen and Shen, Hanwen and Jing, Bowen and Cai, Jiyuan and Wang, Xiaofeng and Lei, Hongsen and Zhou, Weitao and Zhang, Dandan and Yu, Haibao},
  journal       = {arXiv preprint arXiv:2608.27365},
  year          = {2026},
  eprint        = {2608.27365},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2608.27365}
}

Acknowledgments

KnockGS is built on PhysGaussian, 3D Gaussian Splatting, and Warp-MPM. We thank the authors for releasing their work.