ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation
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Cited by 29 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Humans intuitively understand that inanimate objects do not move by themselves, but that state changes are typically caused by human manipulation (e.g., the opening of a book). This is not yet the case for machines. In part this is because there exist no datasets with ground-truth 3D annotations for the study of physically consistent and synchronised motion of hands and articulated objects. To this end, we introduce ARCTIC -- a dataset of two hands that dexterously manipulate objects, containing 2.1M video frames paired with accurate 3D hand and object meshes and detailed, dynamic contact information. It contains bi-manual articulation of objects such as scissors or laptops, where hand poses and object states evolve jointly in time. We propose two novel articulated hand-object interaction tasks: (1) Consistent motion reconstruction: Given a monocular video, the goal is to reconstruct two hands and articulated objects in 3D, so that their motions are spatio-temporally consistent. (2) Interaction field estimation: Dense relative hand-object distances must be estimated from images. We introduce two baselines ArcticNet and InterField, respectively and evaluate them qualitatively and quantitatively on ARCTIC. Our code and data are available at https://arctic.is.tue.mpg.de.
Key facts. Cited by 312 papers in the map (data through 14 Oct 2026). Builds on 58 of its 92 references that are in the map. First posted on arXiv 28 Apr 2022.
Builds on
58 of 92 references.
References · 58
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38 more
- 2022HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interactioncited by 327
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- 2021H2O: Two Hands Manipulating Objects for First Person Interaction Recognitioncited by 254
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- 2020InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Imagecited by 245
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- 2022Embodied Hands: Modeling and Capturing Hands and Bodies Togethercited by 181
- 2020Grasping Field: Learning Implicit Representations for Human Graspscited by 179
- 2019Category-Level Articulated Object Pose Estimationcited by 159
- 2017GANerated Hands for Real-time 3D Hand Tracking from Monocular RGBcited by 147
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- 2019H+O: Unified Egocentric Recognition of 3D Hand-Object Poses and Interactionscited by 141
- 2021Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Timecited by 141
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- 2021ContactOpt: Optimizing Contact to Improve Graspscited by 139
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- 2020CPF: Learning a Contact Potential Field to Model the Hand-Object Interactioncited by 128
- 2019End-to-end Hand Mesh Recovery from a Monocular RGB Imagecited by 124
- 2021Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object Interactions for Accurate 3D Pose Estimationcited by 123
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- 2018Hand Pose Estimation via Latent 2.5D Heatmap Regressioncited by 111
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- 2020Weakly Supervised 3D Hand Pose Estimation via Biomechanical Constraintscited by 97
- 2018Cross-modal Deep Variational Hand Pose Estimationcited by 89
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- 2016Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Inputcited by 56
50 most cited of 58 All 58 (JSON)
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Citing papers per year: 2022: 2; 2023: 37; 2024: 70; 2025: 83; 2026: 120.
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50 most cited of 312 All 312 (JSON)
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- 2026MoSE3: Learning World-Space SE(3) at Every Pixel2 Oct 2026
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- 2026EgoFound3R: End-to-End Egocentric Hand Reconstruction in World Space with Point-Wise Interaction Attributes1 Oct 2026
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- 2026AgentSTAR: Agentic Shape Tracking and Reconstruction from Monocular Videos21 Sep 2026
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50 newest of 312 All 312 (JSON)
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12 most cited of 256 All 256 (JSON)
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12 newest of 256 All 256 (JSON)