HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction
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Abstract (arXiv)
We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the research of category-level human-object interaction. HOI4D consists of 2.4M RGB-D egocentric video frames over 4000 sequences collected by 4 participants interacting with 800 different object instances from 16 categories over 610 different indoor rooms. Frame-wise annotations for panoptic segmentation, motion segmentation, 3D hand pose, category-level object pose and hand action have also been provided, together with reconstructed object meshes and scene point clouds. With HOI4D, we establish three benchmarking tasks to promote category-level HOI from 4D visual signals including semantic segmentation of 4D dynamic point cloud sequences, category-level object pose tracking, and egocentric action segmentation with diverse interaction targets. In-depth analysis shows HOI4D poses great challenges to existing methods and produces great research opportunities.
Key facts. Cited by 321 papers in the map (data through 7 Oct 2026). Builds on 36 of its 54 references that are in the map. First posted on arXiv 3 Mar 2022.
Builds on
36 of 54 references.
References · 36
- 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
- 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
- 2016Generative Adversarial Imitation Learningcited by 1,576
- 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,201
- 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
- 2018Scaling Egocentric Vision: The EPIC-KITCHENS Datasetcited by 789
- 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 768
- 2018Open3D: A Modern Library for 3D Data Processingcited by 717
- 2020SAPIEN: A SimulAted Part-based Interactive ENvironmentcited by 584
- 2018PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understandingcited by 521
- 2019Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimationcited by 490
- 2019Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoningcited by 420
24 more
- 2020GRAB: A Dataset of Whole-Body Human Grasping of Objectscited by 397
- 2019Learning joint reconstruction of hands and manipulated objectscited by 377
- 2019MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentationcited by 370
- 2021DexYCB: A Benchmark for Capturing Hand Grasping of Objectscited by 320
- 2019FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape from Single RGB Imagescited by 271
- 2021Mesh Graphormercited by 264
- 2021H2O: Two Hands Manipulating Objects for First Person Interaction Recognitioncited by 251
- 2017Hand Keypoint Detection in Single Images using Multiview Bootstrappingcited by 231
- 2017First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotationscited by 229
- 2021DexMV: Imitation Learning for Dexterous Manipulation from Human Videoscited by 194
- 2021ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrationscited by 193
- 2020ContactPose: A Dataset of Grasps with Object Contact and Hand Posecited by 180
- 2021ASFormer: Transformer for Action Segmentationcited by 166
- 2019Category-Level Articulated Object Pose Estimationcited by 158
- 2021Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusioncited by 129
- 2019MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequencescited by 117
- 2019DexPilot: Vision Based Teleoperation of Dexterous Robotic Hand-Arm Systemcited by 105
- 2021BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Modelscited by 87
- 2021CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Cloudscited by 83
- 20196-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypointscited by 78
- 20214D Panoptic LiDAR Segmentationcited by 64
- 2017BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysiscited by 46
- 20214D-Net for Learned Multi-Modal Alignmentcited by 44
- 2016Model-based Deep Hand Pose Estimationcited by 11
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Citing papers per year: 2022: 5; 2023: 39; 2024: 60; 2025: 99; 2026: 118.
321 papers.
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50 most cited of 321 All 321 (JSON)
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50 newest of 321 All 321 (JSON)
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12 most cited of 19 All 19 (JSON)
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12 most cited of 257 All 257 (JSON)
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- 2026OmniPoint: Universal Monocular Metric Pointcloud from Any Camera8 Sep 2026
12 newest of 257 All 257 (JSON)