Training-Free Dense Hand Contact Estimation with Multi-Modal Large Language Models
Dense hand contact estimation requires both high-level semantic understanding and fine-grained geometric reasoning of human interaction to accurately localize contact regions. Recently, multi-modal large language models (MLLMs) have demonstrated strong capabilities in understanding visual semantics, enabled by vision-language priors learned from large-scale data. However, leveraging MLLMs for dense hand contact estimation remains underexplored. There are two major challenges in applying MLLMs to dense hand contact estimation. First, encoding explicit 3D hand geometry is difficult, as MLLMs primarily operate on vision and language modalities. Second, capturing fine-grained vertex-level con...
| Authors | Daniel Sungho Jung, Kyoung Mu Lee |
|---|---|
| Primary category | cs.CV |
| Published | 7 May 2026 |
| Last updated at source | 13 October 2026 |
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