sboughorbel/human_behavior_atlas_v2

Human Behavior Atlas v2

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, designed for reinforcement learning training with the verl framework and multimodal language models such as Qwen2.5-Omni-7B.

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

Repository Structure

sboughorbel/human_behavior_atlas_v2/
  train-00000-of-XXXXX.parquet    # Sharded parquet with embedded audio/video
  train-00001-of-XXXXX.parquet
  ...
  validation-*.parquet
  test-*.parquet

All data — including audio, video, and pre-extracted features — is fully embedded in the parquet files. No separate downloads or extraction needed.

Usage

Loading with HuggingFace Datasets

from datasets import load_dataset

# Stream without downloading everything
ds = load_dataset("sboughorbel/human_behavior_atlas_v2", split="train", streaming=True)
sample = next(iter(ds))

# Load a subset
ds_100 = load_dataset("sboughorbel/human_behavior_atlas_v2", split="train[:100]")

# Filter by task or modality
emotion_ds = ds_100.filter(lambda x: x["task"] == "emotion_cls")

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]
    # e.g., with decord:
    # from decord import VideoReader
    # vr = VideoReader(io.BytesIO(video_bytes))

Download and Setup

# Download full dataset
huggingface-cli download sboughorbel/human_behavior_atlas_v2 \
    --repo-type dataset --local-dir /path/to/data

# Or download specific splits only
huggingface-cli download sboughorbel/human_behavior_atlas_v2 \
    --repo-type dataset --local-dir /path/to/data \
    --include "train-*.parquet"

Integration with verl RL Training

This dataset is designed for RL training with verl using Qwen2.5-Omni-7B. The problem field contains structured prompts with <audio> and <video> modality markers. Audio and video bytes are loaded directly from parquet — no path resolution needed.

All data including feature tensors is embedded directly in the parquet files.

# verl training config
python3 -m verl.trainer.main_ppo \
    data.train_files=/path/to/data/train-*.parquet \
    data.val_files=/path/to/data/validation-*.parquet \
    data.prompt_key=problem \
    data.image_key=images \
    data.video_key=videos \
    data.modalities='audio,videos' \
    ...

Citation

If you use this dataset, please cite the following paper:

@article{Ong2025HumanBehavior,
  title={Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understanding},
  author={Ong, Keane and Dai, Wei and Li, Carol and Feng, Dewei and Li, Hengzhi and Wu, Jingyao and Cheong, Jiaee and Mao, Rui and Mengaldo, Gianmarco and Cambria, Erik and Liang, Paul Pu},
  journal={arXiv preprint arXiv:2510.04899},
  year={2025}
}

Keane Ong, Wei Dai, Carol Li, Dewei Feng, Hengzhi Li, Jingyao Wu, Jiaee Cheong, Rui Mao, Gianmarco Mengaldo, Erik Cambria, Paul Pu Liang. "Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understanding." ICLR 2026. arXiv:2510.04899

Please also cite the individual source datasets as appropriate:

• CMU-MOSEI: Zadeh et al., "Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph", ACL 2018
• MELD: Poria et al., "MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations", ACL 2019
• CREMA-D: Cao et al., "CREMA-D: Crowd-Sourced Emotional Multimodal Actors Dataset", IEEE TAC 2014
• DAIC-WOZ: Gratch et al., "The Distress Analysis Interview Corpus of Human and Computer Interviews", LREC 2014
• CH-SIMS v2: Liu et al., "Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module", ICMI 2022

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; please consult the original dataset publications for details.