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HO-3D_v3: Improving the Accuracy of Hand-Object Annotations of the HO-3D Dataset

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arxiv 2107.00887 v1 pith:6XB6B4E6 submitted 2021-07-02 cs.CV cs.HC

Shreyas Hampali , Sayan Deb Sarkar , Vincent Lepetit This is my paper · ORCID
classification cs.CVcs.HC
keywords ho-3dhandobjectaccuracyannotationscontactdatasethand-object
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HO-3D is a dataset providing image sequences of various hand-object interaction scenarios annotated with the 3D pose of the hand and the object and was originally introduced as HO-3D_v2. The annotations were obtained automatically using an optimization method, 'HOnnotate', introduced in the original paper. HO-3D_v3 provides more accurate annotations for both the hand and object poses thus resulting in better estimates of contact regions between the hand and the object. In this report, we elaborate on the improvements to the HOnnotate method and provide evaluations to compare the accuracy of HO-3D_v2 and HO-3D_v3. HO-3D_v3 results in 4mm higher accuracy compared to HO-3D_v2 for hand poses while exhibiting higher contact regions with the object surface.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ProxyPose: 6-DoF Pose Tracking via Video-to-Video Translation

    cs.CV 2026-07 conditional novelty 8.0 of 10

    A fine-tuned video diffusion model translates monocular video into a synthetic proxy video of a moving cube, enabling 6-DoF pose tracking via classical solvers without 3D models, depth, or masks.

  2. Hand-Object Interaction in the Age of Large Foundation Models:Reconstruction, Generation, and Embodied Transfer

    cs.CV 2026-07 conditional novelty 7.0 of 10

    Foundation-model HOI work is organized into eight geometric, semantic, and visual sub-priors that enter six reconstruction/generation tasks and three robot-transfer routes.

  3. ManiVideo: Generating Hand-Object Manipulation Video with Dexterous and Generalizable Grasping

    cs.CV 2024-12 conditional novelty 6.0 of 10

    ManiVideo generates bimanual hand-object manipulation videos conditioned on 3D motion sequences, using a multi-layer occlusion representation and Objaverse-based training to improve 3D consistency and object generalization.

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