mlsanityData to 7 Oct 2026

ADL4D: Towards A Contextually Rich Dataset for 4D Activities of Daily Living

arXiv 2402.17758 ↗First posted 27 Feb 2024cs.CVcited by 1

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Abstract (arXiv)

Hand-Object Interactions (HOIs) are conditioned on spatial and temporal contexts like surrounding objects, previous actions, and future intents (for example, grasping and handover actions vary greatly based on objects proximity and trajectory obstruction). However, existing datasets for 4D HOI (3D HOI over time) are limited to one subject interacting with one object only. This restricts the generalization of learning-based HOI methods trained on those datasets. We introduce ADL4D, a dataset of up to two subjects interacting with different sets of objects performing Activities of Daily Living (ADL) like breakfast or lunch preparation activities. The transition between multiple objects to complete a certain task over time introduces a unique context lacking in existing datasets. Our dataset consists of 75 sequences with a total of 1.1M RGB-D frames, hand and object poses, and per-hand fine-grained action annotations. We develop an automatic system for multi-view multi-hand 3D pose annotation capable of tracking hand poses over time. We integrate and test it against publicly available datasets. Finally, we evaluate our dataset on the tasks of Hand Mesh Recovery (HMR) and Hand Action Segmentation (HAS).

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 41 of its 56 references that are in the map. First posted on arXiv 27 Feb 2024.

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41 of 56 references.

References · 41

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29 more
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  1. 2025Person Identification from Egocentric Human-Object Interactions using 3D Hand Posecited by 0