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hograspnet dense hand object ho graspnet with full grasping taxonomy and dynamics woojin cho 1 jihyun lee 2 minjae yi 2 minje kim 2 taeyun woo 2 donghwan kim 2 taewook ha 1 hyokeun lee 3 je hwan ryu 4 woontack woo 1 5 tae kyun kim 2 6 kaist uvr lab 1 kaist cvl lab 2 kwangwoon university 3 surromind 4 kaist ki itc arrc 5 imperial college london 6 eccv 2024 paper data download code abstract existing datasets for 3d hand object interaction are limited either in the data cardinality data variations in interaction scenarios or the quality of annotations in this work we present a comprehensive new training dataset for hand object interaction called hograspnet it is the only real dataset that captures full grasp taxonomies providing grasp annotation and wide intraclass variations using grasp taxonomies as atomic actions their space and time combinatorial can represent complex hand activities around objects we select 22 rigid objects from the ycb dataset and 8 other compound objects using shape and size taxonomies ensuring coverage of all hand grasp configurations the dataset includes diverse hand shapes from 99 participants aged 10 to 74 continuous video frames and a 1 5m rgb depth of sparse frames with annotations it offers labels for 3d hand and object meshes 3d keypoints contact maps and grasp labels accurate hand and object 3d meshes are obtained by fitting the hand parametric model mano and the hand implicit function halo to multi view rgbd frames with the mocap system only for objects note that halo fitting does not require any parameter tuning enabling scalability to the dataset s size with comparable accuracy to mano we evaluate hograspnet on relevant tasks grasp classification and 3d hand pose estimation the result shows performance variations based on grasp type and object class indicating the potential importance of the interaction space captured by our dataset the provided data aims at learning universal shape priors or foundation models for 3d hand object interaction paper document paper supplementary scanned 3d object models grasp configurations bibtex inproceedings 2024graspnet title dense hand object ho graspnet with full grasping taxonomy and dynamics author cho woojin and lee jihyun and yi minjae and kim minje and woo taeyun and kim donghwan and ha taewook and lee hyokeun and ryu je hwan and woo woontack and kim tae kyun booktitle eccv year 2024 acknowledgements 이 연구는 과학기술정보통신부의 재원으로 한국지능정보사회진흥원의 지원을 받아 구축된 물체 조작 손 동작 3d 데이터 을 활용하여 수행된 연구입니다 본 연구에 활용된 데이터는 ai 허브 에서 다운로드 받으실 수 있습니다 this research paper used datasets from the open ai dataset project ai hub s korea all data information can be accessed through ai hub this page was built using the academic project page template which was adopted from the nerfies project page this website is licensed under a creative commons attribution sharealike 4 0 international license