arxiv: 2605.09538 · v1 · submitted 2026-05-10 · 💻 cs.CV · cs.AI· cs.RO

Recognition: 2 theorem links

· Lean Theorem

PhysHanDI: Physics-Based Reconstruction of Hand-Deformable Object Interactions

Changmin Lee, Donghwan Kim, Jihyun Lee, Tae-Kyun Kim

Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:58 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.RO

keywords hand-object interactiondeformable object reconstructionphysics-based simulation3D reconstructioninverse physicsnon-rigid deformationhand tracking

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The pith

Physically simulating object deformations from 3D hand forces produces coherent reconstructions of hand and non-rigid object interactions, which in turn refines the hand model via inverse physics.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Current reconstruction methods either treat objects as rigid or model deformable ones without complete hand geometry, limiting their use on everyday items like cloth or stuffed animals. PhysHanDI instead reconstructs dense 3D hand motions first, derives contact forces from them, and drives a physics engine to deform the object accordingly. The resulting object motion stays consistent with the hand actions and obeys physical laws. The same simulation loop is then inverted to adjust the hand reconstruction, removing inconsistencies that pure visual methods miss. Tests show gains in both static accuracy and prediction of future states over prior baselines.

Core claim

The paper claims that object deformations can be recovered by forward physics simulation driven by forces from dense 3D hand motion, that this yields dynamics coherent with the hand, and that the same simulation can be run in the inverse direction to correct and improve the hand reconstruction itself.

What carries the argument

Forward physics simulation of non-rigid object deformations induced by forces computed from reconstructed hand motion, with an inverse-physics loop that feeds object state back to refine the hand.

If this is right

Object reconstructions become physically consistent with observed hand contacts rather than relying on visual cues alone.
Hand pose estimates improve when object physics is allowed to constrain them.
Future interaction states can be predicted by continuing the same physics simulation forward in time.
The framework handles fully non-rigid everyday objects without assuming rigidity or part-wise rigidity.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The bidirectional hand-object refinement could extend to robotic grasping of soft items by providing more reliable initial 3D models.
Similar physics feedback loops might stabilize multi-view reconstruction in other deformable settings such as cloth on bodies or tissue in medical imaging.
If the simulation parameters can be learned from data, the method might reduce the need for manual material tuning across different object types.

Load-bearing premise

The chosen physics simulator must faithfully reproduce how real materials bend and stretch under hand contact, and the inverse step must correct hand errors without introducing new ones.

What would settle it

Reconstructed hand motions fed into the simulator produce object deformations that visibly mismatch the shapes seen in the original video frames.

Figures

Figures reproduced from arXiv: 2605.09538 by Changmin Lee, Donghwan Kim, Jihyun Lee, Tae-Kyun Kim.

**Figure 1.** Figure 1: PHYSHANDI models physically plausible hand–deformable object interactions. In our interaction model, each hand is represented by the MANO model (Romero et al., 2017), and each object is represented by a spring–mass model (Liu et al., 2013). Their interaction is modeled by simulating object deformations driven by interaction forces derived from the reconstructed 3D hand motions. Our interaction model can be… view at source ↗

**Figure 2.** Figure 2: Illustration of inverse physics for object reconstruction and hand refinement. Our spring–mass simulation is driven by the spring–mass object model and the MANO hand model. In the object reconstruction stage, the object model is fitted via inverse physics given the initial MANO models, while in the subsequent hand refinement stage, the initial MANO models are refined given the reconstructed object model.… view at source ↗

**Figure 3.** Figure 3: Qualitative comparisons on (1) reconstruction and resimulation, and (2) future prediction. Yellow circles indicate regions where object simulations are less accurately aligned with the ground-truth observations or with the interacting hand contacts. Compared to all the baselines, our method produces more accurate object simulations. More qualitative results are provided in the supplementary video. suboptim… view at source ↗

**Figure 4.** Figure 4: Comparisons in reconstructed spring–mass model topology and force. (1) Topology reconstruction. PhysTwin (Jiang et al., 2025)’s sparser hand points tend to result in excessively long virtual spring lengths to maintain contact coverage, whereas ours based on dense hand points precisely localizes contacts without unnecessary spring elongation—considered a more optimal topology in prior works (Silling & Askar… view at source ↗

**Figure 5.** Figure 5: Captured sequences in PhysTwin (Jiang et al., 2025) and DENSEHDI. The sequences in DENSEHDI feature denser hand–object contacts. B. Method Details In this section, we provide additional details on reconstructing our dense hand–deformable object interaction model from sparse-view RGB-D video inputs, as discussed in Sec. 3.2. B.1. Hand Reconstruction In the hand reconstruction stage, we fit the MANO model (R… view at source ↗

read the original abstract

While existing methods for reconstructing hand-object interactions have made impressive progress, they either focus on rigid or part-wise rigid objects-limiting their ability to model real-world objects (e.g., cloth, stuffed animals) that exhibit highly non-rigid deformations-or model deformable objects without full 3D hand reconstruction. To bridge this gap, we present PhysHanDI (Physics-based Reconstruction of Hand and Deformable Object Interactions), a framework that enables full 3D reconstruction of both interacting hands and non-rigid objects. Our key idea is to physically simulate object deformations driven by forces induced from densely reconstructed 3D hand motions, ensuring that the reconstructed object dynamics are both physically plausible and coherent with the interacting hand movements. Furthermore, we demonstrate that such simulation of object deformations can, in turn, refine and improve hand reconstruction via inverse physics. In experiments, PhysHanDI outperforms the state-of-the-art baseline across reconstruction and future prediction.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

PhysHanDI adds a bidirectional physics loop between dense hand reconstruction and deformable object simulation, but the abstract supplies almost no experimental details to show whether the loop actually improves results or just adds plausible-looking artifacts.

read the letter

The main thing here is a framework that drives a physics simulator with forces from a dense 3D hand reconstruction, then uses the resulting object deformation to run inverse physics and refine the hand poses. That closed loop is the stated novelty over prior work that either treats objects as rigid or reconstructs deformables without full hand geometry. On paper the idea makes sense for cloth or plush toys where contact forces matter, and it directly targets a practical gap in hand-object interaction capture for robotics or AR. The abstract claims better reconstruction and future prediction than baselines, which is the kind of result that could matter if the numbers hold up. What is missing is any description of the actual experiments. No datasets are named, no error metrics are given, and there is no mention of how material parameters are chosen or whether the simulator was calibrated against real deformation captures. The stress-test point lands: if the forward sim uses off-the-shelf FEM or position-based dynamics without per-object tuning, mismatches in stiffness or contact will feed straight back into the hand refinement step and could make both outputs worse rather than better. Without ablations on solver stability, friction modeling, or ground-truth physical measurements, it is hard to tell whether the inverse step is correcting errors or just smoothing them into something that looks physically plausible. The paper is aimed at people already working on physics-informed 3D reconstruction of interactions. A reader who wants to try the method for downstream tasks would need the full implementation details and validation numbers before investing time. It is worth sending to referees because the problem is real and the bidirectional framing is clean, but any serious review will have to press hard on the experimental section to see whether the physics actually delivers measurable gains.

Referee Report

3 major / 1 minor

Summary. The paper presents PhysHanDI, a framework for full 3D reconstruction of interacting hands and non-rigid deformable objects (e.g., cloth or plush toys). The core approach physically simulates object deformations driven by forces from densely reconstructed 3D hand motions to enforce physical plausibility and coherence with hand movements; it further claims that running the simulation in inverse can refine and improve the hand reconstruction. Experiments are asserted to show outperformance over state-of-the-art baselines on both reconstruction accuracy and future prediction tasks.

Significance. If the central claims hold with rigorous validation, the work would meaningfully advance hand-object interaction reconstruction by extending beyond rigid or part-rigid assumptions to handle highly deformable objects while maintaining physical consistency. The bidirectional use of forward simulation for object dynamics and inverse physics for hand refinement is a potentially valuable idea, but its impact depends on demonstrating that the chosen simulator accurately captures real non-rigid contact behavior without introducing unquantified errors.

major comments (3)

[Abstract] Abstract: the claim of outperformance 'across reconstruction and future prediction' is stated without any reference to datasets, quantitative metrics (e.g., Chamfer distance, MPJPE, or deformation error), baselines, or implementation details. This absence prevents assessment of whether the experimental evidence supports the central claims.
The method description (key idea paragraph): the forward physics simulation is asserted to produce deformations that are 'physically plausible and coherent' with hand motions, yet no evidence is supplied that the simulator (FEM, position-based dynamics, or otherwise) was calibrated against real deformation data for the target materials. Without such validation, mismatches in bending stiffness, volume preservation, or contact friction will propagate into both object and hand reconstructions, undermining the bidirectional refinement claim.
The inverse-physics refinement step: the paper states that simulation 'can, in turn, refine and improve hand reconstruction,' but provides no analysis of solver stability, convergence criteria, or potential for drift/inconsistency when the forward model is imperfect. This is load-bearing for the claimed improvement over baselines.

minor comments (1)

Notation for hand-induced forces and material parameters should be defined explicitly early in the method section to avoid ambiguity when describing the simulation pipeline.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We address each major comment point by point below, indicating where revisions have been made to strengthen the presentation of our claims and experimental validation.

read point-by-point responses

Referee: [Abstract] Abstract: the claim of outperformance 'across reconstruction and future prediction' is stated without any reference to datasets, quantitative metrics (e.g., Chamfer distance, MPJPE, or deformation error), baselines, or implementation details. This absence prevents assessment of whether the experimental evidence supports the central claims.

Authors: We agree that the abstract would be clearer with explicit references to the supporting experiments. In the revised version, we have updated the abstract to specify the evaluation datasets (our collected PhysHanDI interaction sequences and public hand-object benchmarks), the quantitative metrics (Chamfer distance for object surfaces, MPJPE for hand joints, and per-vertex deformation error), the state-of-the-art baselines, and high-level implementation details. These additions directly ground the outperformance claims without altering the original abstract length substantially. revision: yes
Referee: The method description (key idea paragraph): the forward physics simulation is asserted to produce deformations that are 'physically plausible and coherent' with hand motions, yet no evidence is supplied that the simulator (FEM, position-based dynamics, or otherwise) was calibrated against real deformation data for the target materials. Without such validation, mismatches in bending stiffness, volume preservation, or contact friction will propagate into both object and hand reconstructions, undermining the bidirectional refinement claim.

Authors: The referee correctly notes that explicit calibration evidence is missing from the key-idea description. Our framework employs a position-based dynamics simulator with material parameters selected from standard references for cloth and plush materials. While the main experiments demonstrate coherence through end-to-end reconstruction accuracy, we did not include a dedicated calibration subsection. We have added a new paragraph in the method section describing parameter selection, qualitative matching of simulated versus captured deformations on held-out sequences, and discussion of how contact friction and stiffness are handled, thereby addressing potential error propagation. revision: yes
Referee: The inverse-physics refinement step: the paper states that simulation 'can, in turn, refine and improve hand reconstruction,' but provides no analysis of solver stability, convergence criteria, or potential for drift/inconsistency when the forward model is imperfect. This is load-bearing for the claimed improvement over baselines.

Authors: We acknowledge that the inverse-physics component would benefit from explicit technical analysis. The manuscript reports quantitative gains from the refinement step, yet omits solver-level details. In the revision we have expanded the corresponding method subsection to describe the optimization procedure, convergence criteria (energy and gradient thresholds), observed iteration counts, and empirical checks for drift across our test sequences. These additions directly support the stability of the bidirectional refinement while preserving the original experimental results. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation uses external physics simulation and iterative inverse refinement

full rationale

The paper's core chain reconstructs dense 3D hand motions, induces forces, runs forward physics simulation on the deformable object, and applies inverse physics to refine the hand poses. This forms an optimization loop but does not reduce any claimed prediction or first-principles result to its own inputs by construction. No self-definitional equations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided abstract and description. The simulation is treated as an external, independent module rather than an ansatz or renaming of known results internal to the paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract does not specify any free parameters, axioms, or invented entities; the framework is described as relying on standard physics simulation and inverse physics.

pith-pipeline@v0.9.0 · 5469 in / 1188 out tokens · 50570 ms · 2026-05-12T03:58:39.159840+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
We represent hands using ... MANO model ... objects using a classical physics-based model (Spring–Mass model ...). ... simulate object deformations via spring–mass system driven by interaction forces induced from the reconstructed hand motions
IndisputableMonolith/Foundation/AlphaCoordinateFixation.lean costAlphaLog_high_calibrated_iff unclear
the force on each mass node ni is modeled as: Fi = Σ Fspring + Fdamping + Fexternal ... vt+1 = vt + Δt Fi/mi

Reference graph

Works this paper leans on

19 extracted references · 19 canonical work pages · 1 internal anchor

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Jung, D. S. and Lee, K. M. Learning dense hand con- tact estimation from imbalanced data. InCoRR, volume arXiv:2505.11152,

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doi:10.48550/ARXIV.2410.11831 SA Conference Papers ’25, December 15–18, 2025, Hong Kong, Hong Kong

Karaev, N., Makarov, I., Wang, J., Neverova, N., Vedaldi, A., and Rupprecht, C. Cotracker3: Simpler and better point tracking by pseudo-labelling real videos. arXiv:2410.11831,

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Loshchilov, I. and Hutter, F. Decoupled weight decay regu- larization. InCoRR, volume abs/1711.05101,

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Qi, M., Zhao, Z., and Ma, H. Human grasp generation for rigid and deformable objects with decomposed vq-vae. arXiv preprint arXiv:2501.05483,

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Taheri, O., Ghorbani, N., Black, M. J., and Tzionas, D. Grab: A dataset of whole-body human grasping of objects. In ECCV 2020,

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Yang, Y ., Zhang, Z., Zhang, X., Zeng, Y ., Li, H., and Zuo, W. Physworld: From real videos to world models of deformable objects via physics-aware demonstration syn- thesis.arXiv preprint arXiv:2510.21447,

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Dataset Details In this section, we present the details of our newly captured dataset, DENSEHDI, introduced in Sec

13 PhysHanDI: Physics-Based Reconstruction of Hand-Deformable Object Interactions A. Dataset Details In this section, we present the details of our newly captured dataset, DENSEHDI, introduced in Sec. 4.1. For data acquisition and pre-processing, we follow the same protocol as PhysTwin (Jiang et al., 2025), using three RealSense D455 RGB-D cameras to reco...

work page 2025
[8]

(a) PhysTwin (Jiang et al., 2025)(b) DenseHDI (ours) Figure 5.Captured sequences in PhysTwin (Jiang et al.,

work page 2025
[9]

to the input multi-view RGB-D videos using the loss function defined in Eq. 2 (Sec. 3.2). L2D, Ld, and Lt are defined as L2 losses, with λd and λt set to 1×10 2 and5×10 5, respectively. To obtain 2D keypoint supervision for computing L2D, we use an off-the-shelf estimator (MediaPipe (Lugaresi et al., 2019)). However, we empirically observe that it yields ...

work page 2019
[10]

The MANO 5See related discussions in prior works, e.g., (Li et al., 2021)

for 1500 steps with a learning rate of 2×10 −3, decaying by a factor of 0.98 every 40 steps. The MANO 5See related discussions in prior works, e.g., (Li et al., 2021). Although the MANO-based estimator predicts full 3D hand shapes and poses, we use only its 2D projections since its depth estimates are ambiguous due to themonocularsetting (e.g., projective...

work page 2021
[11]

Directly following (Jiang et al., 2025), we adopt a hierarchical optimization scheme with (1) asparse (zero-order)stage followed by (2) adense (first-order)stage

representing the deformable object, conditioned on the previously fitted 3D hands. Directly following (Jiang et al., 2025), we adopt a hierarchical optimization scheme with (1) asparse (zero-order)stage followed by (2) adense (first-order)stage. Sparse (zero-order) stage.We optimize the coarse, non-differentiable spring–mass model parameters Θ0 ={T, s glo...

work page 2025
[12]

All other hyperparameters are kept identical to (Jiang et al., 2025)

for 200 iterations with an initial learning rate of1×10 −3. All other hyperparameters are kept identical to (Jiang et al., 2025). B.3. Hand Refinement In this stage, we refine the initial MANO parameters Θh to produce object simulations better aligned with the input observations, using the spring–mass model fitted in the previous stage. An overview of thi...

work page 2025
[13]

The MANO parameters are initialized from the fitting results of the initial hand reconstruction stage, with an initial learning rate of2×10 −5 decayed by 0.99 at each iteration

with 40 optimization steps. The MANO parameters are initialized from the fitting results of the initial hand reconstruction stage, with an initial learning rate of2×10 −5 decayed by 0.99 at each iteration. C. Quantitative Results on the PhysTwin-full Dataset Tab. 6 reports quantitative results on the PhysTwin-full dataset (Jiang et al., 2025). As discusse...

work page 2025
[14]

Our method outperforms the state-of-the-art (Jiang et al.,

7.63 5.52 0.97 0.8426.32 14.42 12.26 2.44 0.6922.80 PHYSHANDI (Ours) 7.30 5.40 0.96 0.84 26.44 13.63 12.04 2.41 0.6822.96 Table 6.Reconstruction & Resimulation and Future Prediction results on the PhysTwin-full dataset (Jiang et al., 2025). Our method outperforms the state-of-the-art (Jiang et al.,

work page 2025
[15]

CD is measured in millimeters, and Track Err

on most metrics. CD is measured in millimeters, and Track Err. is scaled by ×100 for readability. 15 PhysHanDI: Physics-Based Reconstruction of Hand-Deformable Object Interactions We additionally provide a per-sequence breakdown on representative sequences from the PhysTwin-full dataset in Tab. 7, where the sequences are categorized intodense- andsparse-c...

work page 2025
[16]

8.94 1.77 0.79 25.44 PHYSHANDI(Ours) 8.38 1.70 0.82 25.89 Table 8.Generalization to unseen interactions on the PhysTwin-full dataset (Jiang et al., 2025).Our method demonstrates superior generalizability compared to the state of the art (Jiang et al., 2025). E. Contact Consistency Analysis In this section, we provide a quantitative evaluation of contact c...

work page 2025
[17]

Our method achieves higher accuracy than PhysTwin (Jiang et al.,

97.0 97.5 PHYSHANDI (Ours) 98.2 98.3 Table 9.Quantitative comparison on contact consistency.Contact Accuracy (%) is computed against pseudo contact labels constructed from the spatial proximity between object and hand points, with distance thresholds of 5 mm and 10 mm—following protocols similar in spirit to (Grady et al., 2021; Liu et al., 2023). Our met...

work page 2021
[18]

excessive wave dispersion and require very large computer run times

0.040 50 8.86 22.48 BPHYSHANDI (Ours, default)0.002 34.44 24.60 C PHYSHANDI (Ours) 0.001 3 6.81 22.62 D PHYSHANDI (Ours) 0.020 3 4.62 24.18 E PHYSHANDI (Ours) 0.040 3 5.26 23.98 F PHYSHANDI (Ours) 0.002 1 4.47 24.24 G PHYSHANDI (Ours) 0.002 10 4.90 24.09 H PHYSHANDI (Ours) 0.002 50 6.15 23.59 Table 10.Sensitivity to initial hyperparametersused in the zero...

work page 2025
[19]

The remaining overhead in our pipeline comes from the additional Hand Reconstruction and Hand Refinement stages, which PhysTwin does not perform

– 13.80 21.22 – 0.14 PHYSHANDI (Ours) 27.3812.24 17.6312.32 0.11 Table 11.Average per-frame runtime breakdown (in seconds) of our method and PhysTwin (Jiang et al., 2025).Our object reconstruction stages, including zero-order and first-order optimization, and inference-time simulation are slightly faster than the corresponding stages of PhysTwin. The rema...

work page 2025