metadata
language:
- en
- zh
license: cc-by-nc-4.0
size_categories:
- 10K<n<100K
task_categories:
- video-classification
- audio-classification
- text-classification
- question-answering
- visual-question-answering
- any-to-any
pretty_name: Human Behavior Atlas v2
tags:
- multimodal
- emotion-recognition
- sentiment-analysis
- humor-detection
- mental-health
- video-qa
- reinforcement-learning
- verl
- rl-training
- qwen2.5-omni
- audio
- video
- pose-estimation
- opensmile
configs:
- config_name: default
data_files:
- split: train
path: train-*.parquet
- split: validation
path: validation-*.parquet
- split: test
path: test-*.parquet
dataset_info:
features:
- name: problem
dtype: string
- name: answer
dtype: string
- name: images
sequence: binary
- name: videos
sequence: binary
- name: audios
sequence: binary
- name: dataset
dtype: string
- name: modality_signature
dtype: string
- name: ext_video_feats
sequence: binary
- name: ext_audio_feats
sequence: binary
- name: task
dtype: string
- name: class_label
dtype: string
splits:
- name: train
num_examples: 74449
- name: validation
num_examples: 7646
- name: test
num_examples: 18204
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:
- 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.