HumanBehaviorAtlas/human_behavior_atlas

Human Behavior Atlas

A large-scale multimodal dataset for human behavior understanding, spanning emotion recognition, sentiment analysis, humor detection, mental health screening, and video question answering. The dataset integrates 16 source datasets into a unified schema with audio, video, and pre-extracted features.

This dataset was used to train OmniSapiens, a foundation model for social behavior processing.

• Papers:
  • Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understanding (ICLR 2026)
  • OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization (ICML 2026)
• Repository: https://github.com/MIT-MI/human_behavior_atlas

Dataset Summary

Property	Value
Total samples	100,299
Train split	74,449
Validation split	7,646
Test split	18,204
Source datasets	16
Modalities	Text, Audio (.wav bytes), Video (.mp4 bytes), OpenSmile features (.pt bytes), Pose features (.pt bytes) — all embedded in parquet
Languages	English, Chinese (CHSIMSv2)
License	CC BY-NC 4.0

Modality Distribution

Modality Signature	Samples	Percentage
text_video_audio	87,318	87.1%
text_audio	10,431	10.4%
text	2,550	2.5%

Source Datasets

Dataset	Samples	Task	Modality	Description
mosei_senti	22,740	Sentiment classification	text_video_audio	CMU-MOSEI sentiment analysis (negative/neutral/positive)
intentqa	14,158	Video QA	text_video_audio	Intent-driven video question answering
meld_senti	13,518	Sentiment classification	text_video_audio	MELD multimodal sentiment (from Friends TV series)
meld_emotion	13,350	Emotion classification	text_video_audio	MELD multimodal emotion recognition (7 classes)
mosei_emotion	8,545	Emotion classification	text_video_audio	CMU-MOSEI emotion recognition (6 classes)
cremad	7,442	Emotion classification	text_audio	CREMA-D acted emotional speech recognition
siq2	6,394	Video QA	text_video_audio	Social IQ 2.0 social intelligence QA
chsimsv2	4,384	Sentiment classification	text_video_audio	CH-SIMS v2 Chinese multimodal sentiment
tess	2,800	Emotion classification	text_audio	Toronto Emotional Speech Set
urfunny	2,113	Humor classification	text_video_audio	UR-Funny multimodal humor detection
mmpsy_depression	1,275	Depression screening	text_video_audio	Multimodal depression assessment
mmpsy_anxiety	1,275	Anxiety screening	text_video_audio	Multimodal anxiety assessment
mimeqa	801	Video QA	text_video_audio	MIME gesture-based QA
mmsd	687	Humor classification	text	Multimodal sarcasm detection (text only)
ptsd_in_the_wild	628	PTSD detection	text_video_audio	PTSD detection from video interviews
daicwoz	189	Depression screening	text_video_audio	DAIC-WOZ clinical depression interviews

Task Types

Task ID	Description	Datasets
emotion_cls	Emotion classification	mosei_emotion, meld_emotion, cremad, tess
sentiment_cls	Sentiment classification / regression	mosei_senti, meld_senti, chsimsv2
humor_cls	Humor and sarcasm detection	urfunny, mmsd
depression	Depression screening	mmpsy_depression, daicwoz
anxiety	Anxiety screening	mmpsy_anxiety
ptsd	PTSD detection	ptsd_in_the_wild
video_qa	Video question answering	intentqa, siq2, mimeqa

Schema

Each row in the Parquet files contains the following columns:

Column	Type	Description
problem	string	Prompt text with modality markers (<audio>, <video>)
answer	string	Ground truth answer
audios	list[bytes]	Raw .wav audio bytes (embedded)
videos	list[bytes]	Raw .mp4 video bytes (embedded)
images	list[bytes]	Image bytes (currently unused)
dataset	string	Source dataset name
modality_signature	string	Modality combination: text_video_audio, text_audio, or text
ext_video_feats	list[bytes]	Pose estimation feature tensors (.pt bytes, embedded)
ext_audio_feats	list[bytes]	OpenSmile audio feature tensors (.pt bytes, embedded)
task	string	Task type identifier
class_label	string	Classification label

Usage

Loading with HuggingFace Datasets

from datasets import load_dataset

# Stream without downloading everything
ds = load_dataset("HumanBehaviorAtlas/human_behavior_atlas", split="train", streaming=True)
sample = next(iter(ds))

# Load a subset
ds_100 = load_dataset("HumanBehaviorAtlas/human_behavior_atlas", split="train[:100]")

Accessing Embedded Media

import io
import soundfile as sf

sample = ds_100[0]

# Audio is raw bytes — decode with soundfile or torchaudio
if sample["audios"]:
    audio_data, sr = sf.read(io.BytesIO(sample["audios"][0]))

# Video is raw bytes — decode with decord, opencv, or write to temp file
if sample["videos"]:
    video_bytes = sample["videos"][0]

Example Entry

{
  "problem": "<audio>
Don't forget a jacket.
The above is a speech recording along with the transcript from a clinical context. What emotion is the speaker expressing? Answer with one word from the following: anger, disgust, fear, happy, neutral, sad",
  "answer": "sad",
  "images": [],
  "videos": [],
  "audios": ["..."],
  "dataset": "cremad",
  "modality_signature": "text_audio",
  "task": "emotion_cls",
  "class_label": "sad"
}

Citation

@inproceedings{
ong2026human,
title={Human Behavior Atlas: Benchmarking Unified Psychological And Social Behavior Understanding},
author={Keane Ong and Wei Dai and Carol Li and Dewei Feng and Hengzhi Li and Jingyao Wu and Jiaee Cheong and Rui Mao and Gianmarco Mengaldo and Erik Cambria and Paul Pu Liang},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=ZKE23BBvlQ}
}

@article{ong2026omnisapiens,
  title={Omnisapiens: A foundation model for social behavior processing via heterogeneity-aware relative policy optimization},
  author={Ong, Keane and Boughorbel, Sabri and Xiao, Luwei and Ekbote, Chanakya and Dai, Wei and Qu, Ao and Wu, Jingyao and Mao, Rui and Hoque, Ehsan and Cambria, Erik and others},
  journal={arXiv preprint arXiv:2602.10635},
  year={2026}
}

License

This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. Individual source datasets may have their own licensing terms.