arxiv: 2604.02736 · v3 · submitted 2026-04-03 · 💻 cs.CV

Recognition: no theorem link

THOM: Generating Physically Plausible Hand-Object Meshes From Text

Hyung Jin Chang, Kwang In Kim, Seungryul Baek, Uyoung Jeong, Yihalem Yimolal Tiruneh

Authors on Pith no claims yet

Pith reviewed 2026-05-13 20:40 UTC · model grok-4.3

classification 💻 cs.CV

keywords hand-object interactiontext-to-3DGaussian splattingphysics-based optimizationmesh extractionphysically plausibletraining-free generation

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The pith

THOM generates physically plausible 3D hand-object meshes from text prompts in a training-free two-stage process.

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

The paper seeks to create 3D hand-object interaction meshes that look realistic, align with text descriptions, and obey basic physical rules like stable contact and no penetration. It tackles the problem that extracting meshes from text-driven Gaussian representations often produces shapes too messy for reliable physics simulation. THOM first builds separate hand and object Gaussians from the prompt, then applies physics-based optimization to refine their interaction. A custom mesh extraction step with direct vertex-to-Gaussian correspondence supplies the topology information needed to regularize the optimization and keep contacts plausible. This matters for downstream uses such as robotic planning and virtual reality where generated objects must actually be graspable.

Core claim

THOM is a training-free pipeline that first produces hand and object 3D Gaussians conditioned on text, then extracts meshes via an explicit vertex-to-Gaussian mapping that permits topology-aware regularization, and finally improves physical plausibility through contact-aware optimization plus vision-language model-guided translation refinement, yielding HOI meshes that satisfy visual realism, text fidelity, and interaction stability without any template object meshes.

What carries the argument

Mesh extraction with explicit vertex-to-Gaussian mapping that supplies topology-aware regularization for subsequent physics-based optimization.

If this is right

Hand-object meshes can be produced directly from arbitrary text prompts without supplying pre-existing object templates.
Topology-aware regularization makes Gaussian-to-mesh conversion stable enough for contact-aware physics refinement.
VLM-guided translation adjustments further reduce interpenetrations and improve grasp realism.
The full pipeline remains training-free, allowing immediate use on new prompts without dataset-specific fine-tuning.

Where Pith is reading between the lines

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

Similar explicit mapping techniques could be tested on full-body or multi-object scenes to extend physical plausibility beyond hands.
The generated meshes could serve as starting points for real-world robot grasp planning if transferred to a physics engine that models friction and deformation.
Integration with real-time rendering pipelines might allow on-the-fly generation of interactive AR content from spoken instructions.

Load-bearing premise

The explicit vertex-to-Gaussian mapping reliably supplies topology information that keeps the physics optimization stable and produces plausible contacts.

What would settle it

Running the extracted meshes through a rigid-body simulator and observing frequent failures such as objects passing through the hand or unstable resting poses would falsify the claim that the method produces physically plausible interactions.

Figures

Figures reproduced from arXiv: 2604.02736 by Hyung Jin Chang, Kwang In Kim, Seungryul Baek, Uyoung Jeong, Yihalem Yimolal Tiruneh.

**Figure 1.** Figure 1: THOM generates photorealistic and physically plausible 3D hand-object interactions from text prompts, with consistent hand and object meshes and realistic contact across diverse objects and hand appearances. Abstract Generating photorealistic 3D hand-object interactions (HOIs) from text is important for applications like robotic grasping and AR/VR content creation. In practice, however, achieving both vis… view at source ↗

**Figure 2.** Figure 2: Overall pipeline of THOM. In the first stage, object and hand Gaussians are separately generated from the text prompts. During [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Qualitative comparisons of ProlificDreamer [ [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: Qualitative comparison with human-object interaction methods. *: Adapted to generate hand-object interactions. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: VLM refinement results. Left: “A right hand calling a [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: User preference study for THOM. “A right hand using a tambourine” “A right hand inspecting Paddington Bear” “A right hand with intricate black tribal tattoo brushing a simple burgundy colored feather quill” “A right hand eating a stack of pancakes” “A right hand lifting a bluebird” “A right hand with gold bangles stacked along the wrist touching a sleek black top hat” [PITH_FULL_IMAGE:figures/full_fig_p01… view at source ↗

**Figure 7.** Figure 7: Diverse generation results of our method from various HOI prompts. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗

**Figure 11.** Figure 11: Failure cases of our method. For each HOI sample, the [PITH_FULL_IMAGE:figures/full_fig_p015_11.png] view at source ↗

**Figure 9.** Figure 9: Comparison of concise mesh extraction methods. Input [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗

**Figure 10.** Figure 10: Qualitative results of our concise mesh extraction. [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗

**Figure 12.** Figure 12: Exemplar VLM prompting inputs. Upper images: In [PITH_FULL_IMAGE:figures/full_fig_p017_12.png] view at source ↗

read the original abstract

Generating photorealistic 3D hand-object interactions (HOIs) from text is important for applications like robotic grasping and AR/VR content creation. In practice, however, achieving both visual fidelity and physical plausibility remains difficult, as mesh extraction from text-generated Gaussians is inherently ill-posed and the resulting meshes are often unreliable for physics-based optimization. We present THOM, a training-free framework that generates physically plausible 3D HOI meshes directly from text prompts, without requiring template object meshes. THOM follows a two-stage pipeline: it first generates hand and object Gaussians guided by text, and then refines their interaction using physics-based optimization. To enable reliable interaction modeling, we introduce a mesh extraction method with an explicit vertex-to-Gaussian mapping, which enables topology-aware regularization. We further improve physical plausibility through contact-aware optimization and vision-language model (VLM)-guided translation refinement. Extensive experiments show that THOM produces high-quality HOIs with strong text alignment, visual realism, and interaction plausibility.

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

THOM's vertex-to-Gaussian mapping plus physics and VLM steps form a coherent training-free pipeline for text-to-HOI meshes, but the absence of metrics leaves the physical-plausibility claim untested.

read the letter

THOM gives a training-free pipeline that starts with text-guided Gaussian generation for hands and objects, then pulls out meshes via a new explicit vertex-to-Gaussian mapping, and finishes with physics-based contact optimization and VLM refinement for better interactions. The fresh part is that vertex-to-Gaussian mapping. It turns the usually messy Gaussian-to-mesh conversion into something that supports topology-aware regularization, which then lets the physics step work more reliably without template meshes. Adding contact awareness and VLM-guided fixes on top is a reasonable way to push physical plausibility in a field where most methods still struggle with that. The paper does a decent job laying out why previous approaches fall short on reliable meshes for optimization. The overall flow makes sense for practical uses like robotic grasping sims or AR/VR. The soft spot is the evidence. The abstract says extensive experiments show strong results on text alignment, realism, and plausibility, but without any numbers, baselines, or ablation details visible, it's hard to gauge how much better this is or whether the mapping actually delivers on the regularization promise. That leaves the central claim a bit thin until the full results are checked. This paper is for computer vision folks working on generative models for 3D hand-object scenes. Someone building on text-to-3D or needing plausible interactions could find the pipeline useful to build from. It deserves a serious referee. The method is grounded in a clear problem and offers a distinct combination of techniques, so review would help tighten the evaluation and see if the claims hold up.

Referee Report

2 major / 0 minor

Summary. The manuscript introduces THOM, a training-free two-stage framework for generating 3D hand-object interaction (HOI) meshes from text prompts. Stage one produces hand and object Gaussians conditioned on text; stage two extracts meshes via a novel explicit vertex-to-Gaussian mapping that supports topology-aware regularization, followed by contact-aware physics optimization and VLM-guided translation refinement. The central claim is that this pipeline yields meshes with strong text alignment, visual realism, and physical plausibility without template meshes or training.

Significance. If the quantitative claims hold, the work would be significant for text-to-3D HOI generation. It removes the requirement for object templates and supervised training while directly targeting physical plausibility through an explicit mapping that addresses the ill-posed Gaussian-to-mesh conversion. The training-free design and the vertex-to-Gaussian component are concrete technical strengths that could transfer to robotic grasping and AR/VR pipelines.

major comments (2)

[Abstract] Abstract: the statement that 'extensive experiments show that THOM produces high-quality HOIs with strong text alignment, visual realism, and interaction plausibility' is unsupported by any reported metrics, baselines, ablation studies, or quantitative comparisons in the provided text. Without these, the central claim of physical plausibility cannot be evaluated.
[Mesh extraction method] Mesh extraction method: the explicit vertex-to-Gaussian mapping is presented as the key enabler of reliable topology-aware regularization for physics-based optimization, yet the manuscript supplies neither the precise mapping equations nor ablation results isolating its contribution relative to standard Gaussian-to-mesh conversions. This leaves the weakest assumption unverified.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will incorporate revisions to strengthen the presentation of our results and technical details.

read point-by-point responses

Referee: [Abstract] Abstract: the statement that 'extensive experiments show that THOM produces high-quality HOIs with strong text alignment, visual realism, and interaction plausibility' is unsupported by any reported metrics, baselines, ablation studies, or quantitative comparisons in the provided text. Without these, the central claim of physical plausibility cannot be evaluated.

Authors: We agree that the abstract claim should be directly supported by evidence. The full manuscript (Section 4) reports quantitative results with metrics for text alignment (CLIP similarity scores), visual realism (user studies and perceptual metrics), and physical plausibility (penetration depth, contact area, and force metrics), along with baseline comparisons. We will revise the abstract to reference these specific quantitative findings and include key numerical results to substantiate the claims. revision: yes
Referee: [Mesh extraction method] Mesh extraction method: the explicit vertex-to-Gaussian mapping is presented as the key enabler of reliable topology-aware regularization for physics-based optimization, yet the manuscript supplies neither the precise mapping equations nor ablation results isolating its contribution relative to standard Gaussian-to-mesh conversions. This leaves the weakest assumption unverified.

Authors: We acknowledge that the current manuscript describes the vertex-to-Gaussian mapping at a high level without the full equations or dedicated ablations. We will add the precise mathematical formulation of the mapping (including the explicit vertex-to-Gaussian correspondence and topology regularization terms) to Section 3.2. We will also include a new ablation study in Section 4 comparing our mapping against standard conversions (e.g., Marching Cubes) on regularization stability and downstream physical plausibility metrics. This will directly verify its contribution. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The provided abstract and context describe THOM as introducing a new mesh extraction method with explicit vertex-to-Gaussian mapping to address an acknowledged ill-posed problem in Gaussian-to-mesh conversion. This constitutes an independent technical contribution rather than a reduction of outputs to inputs by definition, fitted parameters renamed as predictions, or load-bearing self-citations. No equations, uniqueness theorems, or prior-author citations are invoked to force the central claims; the two-stage pipeline (text-guided Gaussian generation followed by physics-based refinement with topology-aware regularization and VLM guidance) stands as self-contained without circular reduction. The reader's assessment of score 2 aligns with minor framework novelty but does not trigger any enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review limited to abstract; specific free parameters in optimization weights or regularization strengths are not detailed. The central claim rests on the domain assumption that physics-based contact optimization applied to extracted meshes yields plausible interactions.

axioms (1)

domain assumption Physics-based optimization applied after mesh extraction from Gaussians produces physically plausible hand-object contacts
Invoked in the second stage of the pipeline to refine interactions.

pith-pipeline@v0.9.0 · 5498 in / 1278 out tokens · 48953 ms · 2026-05-13T20:40:22.665705+00:00 · methodology

discussion (0)

Reference graph

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