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arXiv · 2604.02736

THOM: Generating Physically Plausible Hand-Object Meshes From Text

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

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Uyoung Jeong, Yihalem Yimolal Tiruneh, Hyung Jin Chang, Seungryul Baek, Kwang In Kim. 2026-04-03. THOM: Generating Physically Plausible Hand-Object Meshes From Text. https://arxiv.org/abs/2604.02736

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