Pith. sign in

REVIEW 5 cited by

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2203.01577 v4 pith:X2X67WZ2 submitted 2022-03-03 cs.CV

HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction

Yunze Liu , Yun Liu , Che Jiang , Kangbo Lyu , Weikang Wan , Hao Shen , Boqiang Liang , Zhoujie Fu
show 2 more authors
He Wang Li Yi
This is my paper
classification cs.CV
keywords category-levelhoi4degocentricobjectsegmentationinteractionposeaction
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 5 Pith papers

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

  1. SpaMEM: Benchmarking Dynamic Spatial Reasoning via Perception-Memory Integration in Embodied Environments

    cs.CV 2026-04 unverdicted novelty 7.0

    SpaMEM benchmark shows multimodal LLMs succeed at spatial tasks with text histories but sharply fail at long-horizon belief maintenance from raw visual streams alone.

  2. HandsOnWorld: Unconstrained Egocentric Video Generation with Camera-Disentangled Hand Control

    cs.CV 2026-07 unverdicted novelty 6.0

    HandsOnWorld creates a hand-controlled egocentric video generator from unconstrained monocular video via a new EgoVid-Pro dataset from monocular reconstruction and a Plücker Hand Map that disentangles camera and hand motion.

  3. HumanNet: Scaling Human-centric Video Learning to One Million Hours

    cs.CV 2026-05 unverdicted novelty 6.0

    HumanNet is a 1M-hour human-centric video dataset with interaction annotations that enables better vision-language-action model performance than equivalent robot data in a controlled test.

  4. SpaMEM: Benchmarking Dynamic Spatial Reasoning via Perception-Memory Integration in Embodied Environments

    cs.CV 2026-04 conditional novelty 6.0

    SpaMEM is a diagnostic benchmark showing that current vision-language models exhibit a sharp collapse in spatial reasoning when transitioning from text-aided state tracking to purely visual memory in dynamic environments.

  5. World Action Models: The Next Frontier in Embodied AI

    cs.RO 2026-05 unverdicted novelty 4.0

    The paper introduces World Action Models as a new paradigm unifying predictive world modeling with action generation in embodied foundation models and provides a taxonomy of existing approaches.