mlsanityData to 14 Oct 2026

CHORD: Category-level Hand-held Object Reconstruction via Shape Deformation

arXiv 2308.10574 ↗First posted 21 Aug 2023cs.CVcited by 11

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Abstract (arXiv)

In daily life, humans utilize hands to manipulate objects. Modeling the shape of objects that are manipulated by the hand is essential for AI to comprehend daily tasks and to learn manipulation skills. However, previous approaches have encountered difficulties in reconstructing the precise shapes of hand-held objects, primarily owing to a deficiency in prior shape knowledge and inadequate data for training. As illustrated, given a particular type of tool, such as a mug, despite its infinite variations in shape and appearance, humans have a limited number of 'effective' modes and poses for its manipulation. This can be attributed to the fact that humans have mastered the shape prior of the 'mug' category, and can quickly establish the corresponding relations between different mug instances and the prior, such as where the rim and handle are located. In light of this, we propose a new method, CHORD, for Category-level Hand-held Object Reconstruction via shape Deformation. CHORD deforms a categorical shape prior for reconstructing the intra-class objects. To ensure accurate reconstruction, we empower CHORD with three types of awareness: appearance, shape, and interacting pose. In addition, we have constructed a new dataset, COMIC, of category-level hand-object interaction. COMIC contains a rich array of object instances, materials, hand interactions, and viewing directions. Extensive evaluation shows that CHORD outperforms state-of-the-art approaches in both quantitative and qualitative measures. Code, model, and datasets are available at https://kailinli.github.io/CHORD.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 52 of its 63 references that are in the map. First posted on arXiv 21 Aug 2023.

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52 of 63 references.

References · 52

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38 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 3; 2026: 3.

11 papers.

  1. 2025ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learningcited by 66
  2. 2024SemGrasp: Semantic Grasp Generation via Language Aligned Discretizationcited by 29
  3. 2024ManiDext: Hand-Object Manipulation Synthesis via Continuous Correspondence Embeddings and Residual-Guided Diffusioncited by 21
  4. 2025HORT: Monocular Hand-held Objects Reconstruction with Transformerscited by 11
  5. 2024DICE: End-to-end Deformation Capture of Hand-Face Interactions from a Single Imagecited by 9
  6. 2024SDFit: 3D Object Pose and Shape by Fitting a Morphable SDF to a Single Imagecited by 9
  7. 2023Ins-HOI: Instance Aware Human-Object Interactions Recoverycited by 7
  8. 2026DeltaDorsal: Enhancing Hand Pose Estimation with Dorsal Features in Egocentric Viewscited by 1
  9. 2026Hand-Object Interaction in the Age of Large Foundation Models:Reconstruction, Generation, and Embodied Transfercited by 1
  10. 2025PartHOI: Part-based Hand-Object Interaction Transfer via Generalized Cylinderscited by 0
  11. 2026StableGrasp: Reconstructing Physically Stable Human Hand Grasps from Single Imagescited by 0