README.md

---
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:** 
  - [Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understanding (ICLR 2026)](https://arxiv.org/abs/2510.04899)
  - [OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization (ICML 2026)](https://huggingface.co/papers/2602.10635)
- **Repository:** [https://github.com/MIT-MI/human_behavior_atlas](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

```python
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

```python
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

```json
{
  "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

```bibtex
@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)](https://creativecommons.org/licenses/by-nc/4.0/) license. Individual source datasets may have their own licensing terms.