Transferring human demonstrations to dexterous robots remains challenging because differences in hand morphology and contact dynamics often cause retargeted motions to fail at producing the intended object behavior. We present \textbf{FoLD}, a framework for learning dexterous manipulation of articulated objects through...
Hao-Wei Shen, Ti-Ngai Li, Yu-Meng Liu et al.· 0 citations
A conditional Diffusion Transformer that operates directly in the proposed InterMASH representation space and jointly generates hand geometry and contact, improving consistency and physical plausibility and shows that cross-embodiment fine-tuning with human grasp data can improve robotic grasp success and diversity.
Xuan-Ze Yang, Yu-Meng Liu, Hai-Yang Xin et al.· 0 citations
CGGT is proposed, a Curve-Grounded Geometry Transformer that directly grounds 3D-consistent 2D curve instances in the image space from sparse, unposed multi-view images, demonstrating its potential for practical CAD-style wireframe reconstruction from unconstrained visual inputs.
Zhirui Gao, Renjiao Yi, Yunfan Ye et al.· 0 citations
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