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YAML Metadata Warning:The task_ids "text-to-motion" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation

YAML Metadata Warning:The task_ids "human-object-interaction" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation

CoRoleHOI Dataset

Overview

CoRoleHOI is a dataset for role-asymmetric multi-person multi-object Human–Object Interaction (HOI).

It is designed to support research in:

  • Text-driven motion generation
  • Multi-person interaction modeling
  • Human-object interaction understanding
  • Embodied AI and robotics

The dataset integrates multi-modal signals:

  • SMPL-X human motion sequences
  • Object trajectories (4×4 transformation matrices)
  • Fine-grained contact annotations
  • Structured textual prompts
  • Object meshes and SDF representations

Key Contributions

  • Role-asymmetric interaction modeling (e.g., giver → receiver)
  • Multi-person coordination scenarios
  • Multi-object interaction support
  • Fine-grained contact supervision
  • Aligned text–motion–object representation

Dataset Structure

The dataset is distributed primarily as a compressed archive:

CoRoleHOI/ ├── data.zip ├── README.md ├── croissant.json

Contents of data.zip:

data/ ├── smplx_npz/ ├── obj_transmat_npy/ ├── contact_labels_w_semantics_npy_files/ ├── text_anno_json_data/ ├── human_metadata/ ├── obj_metadata/ ├── captured_objects/ ├── rest_object_sdf_256_npy_files/ ├── augmented_prompts.json ├── prompt_dict.json ├── split_prompts.json


Data Modalities

Each interaction sequence includes:

Modality Description
Human Motion SMPL-X parameters
Object Motion 4×4 transformation matrices
Contact Labels Binary / semantic contact
Text Natural language interaction description
Metadata Human & object attributes

Text Annotations

The dataset provides structured language supervision:

  • augmented_prompts.json
    Multiple paraphrases per interaction

  • prompt_dict.json
    Prompt ID to canonical text mapping

  • split_prompts.json
    Train / validation / test splits


Example Interaction

Person A passes the printer to Person B.


Download

Dataset page:
https://huggingface.co/datasets/RayZuo/CoRoleHOI

Direct download:
https://huggingface.co/datasets/RayZuo/CoRoleHOI/resolve/main/CoRoleHOI.zip

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