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HIMO: A New Benchmark for Full-Body Human Interacting with Multiple Objects

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Xintao Lv, Liang Xu, Yichao Yan, Xin Jin, Congsheng Xu, Shuwen Wu, Yifan Liu, Lincheng Li
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Mengxiao Bi, Wenjun Zeng, Xiaokang Yang

Computer Vision and Pattern Recognition · Submitted 17 Jul 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Experimental results demonstrate the generalization ability to unseen object geometries and temporal compositions.

how it is stated · asserted 1.00 read the record →

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arXiv ID2407.12371 (version 2)
Pith IDQOEOFKZC, Pith's permanent record of this paper and its review
CitationCongsheng Xu, Liang Xu, Lincheng Li, Mengxiao Bi, Shuwen Wu, Wenjun Zeng, Xiaokang Yang, Xin Jin, Xintao Lv, Yichao Yan, Yifan Liu. "HIMO: A New Benchmark for Full-Body Human Interacting with Multiple Objects." arXiv:2407.12371 (2024). https://pith.science/paper/QOEOFKZC
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,547 input tokens.

What the abstract itself states, as probabilities: claims first 0.19 · states limitation 0.10 · human participants 0.44 · controlled comparison 0.12 · randomized assignment 0.01 · states quantitative result 0.92 · compares with prior results 0.19 · mentions code or data availability 0.06.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

Generating human-object interactions (HOIs) is critical with the tremendous advances of digital avatars. Existing datasets are typically limited to humans interacting with a single object while neglecting the ubiquitous manipulation of multiple objects. Thus, we propose HIMO, a large-scale MoCap dataset of full-body human interacting with multiple objects, containing 3.3K 4D HOI sequences and 4.08M 3D HOI frames. We also annotate HIMO with detailed textual descriptions and temporal segments, benchmarking two novel tasks of HOI synthesis conditioned on either the whole text prompt or the segmented text prompts as fine-grained timeline control. To address these novel tasks, we propose a dual-branch conditional diffusion model with a mutual interaction module for HOI synthesis. Besides, an auto-regressive generation pipeline is also designed to obtain smooth transitions between HOI segments. Experimental results demonstrate the generalization ability to unseen object geometries and temporal compositions.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Generating human-object interactions (HOIs) is critical with the tremendous advances of digital avatars. background 1.00 0.05 0.03 asserted 1.00
s1 Existing datasets are typically limited to humans interacting with a single object while neglecting the ubiquitous manipulation of multiple objects. gap 0.98 0.06 0.04 asserted 1.00
s2 Thus, we propose HIMO, a large-scale MoCap dataset of full-body human interacting with multiple objects, containing 3.3K 4D HOI sequences and 4.08M 3D HOI frames. contribution 0.99 0.96 0.17 asserted 1.00
s3 We also annotate HIMO with detailed textual descriptions and temporal segments, benchmarking two novel tasks of HOI synthesis conditioned on either the whole text prompt or the segmented text prompts as fine-grained timeline control. contribution 0.94 0.96 0.08 asserted 1.00
s4 To address these novel tasks, we propose a dual-branch conditional diffusion model with a mutual interaction module for HOI synthesis. method 0.91 0.93 0.07 asserted 0.99
s5 Besides, an auto-regressive generation pipeline is also designed to obtain smooth transitions between HOI segments. method 0.97 0.91 0.03 asserted 0.99
s6 Experimental results demonstrate the generalization ability to unseen object geometries and temporal compositions. result 0.95 0.94 0.61 asserted 1.00

What kind of paper the classifier reports this is: dataset or benchmark 1.00 · evidence from observation or experiment 0.97.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Generating human-object interactions (HOIs) is critical with the tremendous advances of digital avatars. Existing datasets are typically limited to humans interacting with a single object while neglecting the ubiquitous manipulation of multiple objects. Thus, we propose HIMO, a large-scale MoCap dataset of full-body human interacting with multiple objects, containing 3.3K 4D HOI sequences and 4.08M 3D HOI frames. We also annotate HIMO with detailed textual descriptions and temporal segments, benchmarking two novel tasks of HOI synthesis conditioned on either the whole text prompt or the segmented text prompts as fine-grained timeline control. To address these novel tasks, we propose a dual-branch conditional diffusion model with a mutual interaction module for HOI synthesis. Besides, an auto-regressive generation pipeline is also designed to obtain smooth transitions between HOI segments. Experimental results demonstrate the generalization ability to unseen object geometries and temporal compositions.

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