Improving and Evaluating Hand-Object Interaction Detection

Ahmad Darkhalil, Dima Damen, David Fouhey
6/16/2026
cs.CV

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Abstract

Understanding hands and the objects they interact with, both directly and through tools, is a key step for tasks ranging from action perception to 3D reconstruction and robotics. Our paper provides several contributions to the Hand-Object Interaction (HOI) understanding literature: (1) HOI-DETR, a new framework that introduces hand-object and object-object interactions to the Co-DETR architecture to produce a state-of-the-art method; (2) a comprehensive HOI evaluation suite of 4 diverse datasets, including a video benchmark derived from the HD-EPIC dataset and fresh annotations that improve the Hands23 benchmark and (3) a trained checkpoint that significantly improves the state of the art across Hands23, HOIST, FineBio, and HD-EPIC, including mAP gains of over 20 percentage points on Hands23 and FineBio. Our ablations confirm the contributions of each model component.

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Cite this paper

@article{darkhalil2026improving,
  title  = {Improving and Evaluating Hand-Object Interaction Detection},
  author = {Ahmad Darkhalil and Dima Damen and David Fouhey},
  year   = {2026},
  eprint = {2606.17384},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2606.17384v1}
}

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