---
dataset_info:
  features:
  - name: channel_id
    dtype: string
  - name: video_id
    dtype: string
  - name: segment_id
    dtype: int32
  - name: duration
    dtype: string
  - name: fps
    dtype: int32
  - name: conversation
    sequence:
    - name: utterance_id
      dtype: int32
    - name: speaker
      dtype: int32
    - name: text
      dtype: string
    - name: start_time
      dtype: float32
    - name: end_time
      dtype: float32
    - name: words
      sequence:
      - name: word
        dtype: string
      - name: start_time
        dtype: float32
      - name: end_time
        dtype: float32
  - name: facial_expression
    sequence:
    - name: utt_id
      dtype: string
    - name: frame
      dtype: string
    - name: features
      sequence: float32
  - name: body_language
    sequence:
    - name: utt_id
      dtype: string
    - name: frame
      dtype: string
    - name: features
      sequence: float32
  - name: harmful_utterance_id
    sequence: int32
  - name: speaker_bbox
    list:
    - name: bbox
      sequence: int64
    - name: frame_id
      dtype: int64
  splits:
  - name: train
    num_bytes: 13538714571
    num_examples: 800
  - name: test
    num_bytes: 3766543145
    num_examples: 200
  download_size: 16484082115
  dataset_size: 17305257716
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

## Dataset Card for VENUS

### Dataset Summary

Data from: Speaking Beyond Language: A Large-Scale Multimodal Dataset for Learning Nonverbal Cues from Video-Grounded Dialogues

```
@article{kim2025speaking,
  title={Speaking Beyond Language: A Large-Scale Multimodal Dataset for Learning Nonverbal Cues from Video-Grounded Dialogues},
  author={Kim, Youngmin and Chung, Jiwan and Kim, Jisoo and Lee, Sunghyun and Lee, Sangkyu and Kim, Junhyeok and Yang, Cheoljong and Yu, Youngjae},
  journal={arXiv preprint arXiv:2506.00958},
  year={2025}
}

```

We provide a multimodal large-scale video dataset based on nonverbal communication.

Please cite our work if you find our data helpful.

Our dataset collection pipeline and the model implementation that uses it are available at <a href='https://github.com/winston1214/nonverbal-conversation'>https://github.com/winston1214/nonverbal-conversation</a>

### Dataset Statistic

| Split        | Channels   | Videos | Segments (10 min)    | Frames (Nonverbal annotations)   | Utterances   | Words     |
|:---------------:|:--------:|:---------:|:-------:|:-------:|:----------:|:----------:|
| Train      |  12  | 293     | 800  | 9,727,175     | 11,855        | 1,385,404        |
| Test       | 4   | 113     | 200   | 2,651,674     | 4,356        | 393,752        |


### Language

English

### Other Version

- **VENUS-5K**: <a href='https://huggingface.co/datasets/winston1214/VENUS-5K'>This link</a>
- **VENUS-10K**: <a href='https://huggingface.co/datasets/winston1214/VENUS-10K'>This link</a>
- **VENUS-25K**: <a href='https://huggingface.co/datasets/winston1214/VENUS-25K'>This link</a> ***Comming Soon!***
- **VENUS-50K**: <a href=''>This link</a> ***Comming Soon!***
- **VENUS-100K** (Full): <a href=''>This link</a> ***Comming Soon!***

### Data Structure

Here's an overview of our dataset structure:

```
{
    'channel_id': str,  # YouTube channel ID
    'video_id': str,  # Video ID
    'segment_id': int,  # Segment ID within the video
    'duration': str,  # Total segment duration (e.g., '00:11:00 ~ 00:21:00')
    'fps': int,  # Frames per second

    'conversation': [  # Conversation information (consisting of multiple utterances)
        {
            'utterance_id': int,  # Utterance ID
            'speaker': int,  # Speaker ID (represented as an integer)
            'text': str,  # Full utterance text
            'start_time': float,  # Start time of the utterance (in seconds)
            'end_time': float,  # End time of the utterance (in seconds)
            'words': [  # Word-level information
                {
                    'word': str,  # The word itself
                    'start_time': float,  # Word-level start time
                    'end_time': float,  # Word-level end time
                }
            ]
        }
    ],

    'facial_expression': [  # Facial expression features
        {
            'utt_id': str,  # ID of the utterance this expression is aligned to
            'frame': str,  # Frame identifier
            'features': [float],  # Facial feature vector (153-dimensional)
        }
    ],

    'body_language': [  # Body language features
        {
            'utt_id': str,  # ID of the utterance this body language is aligned to
            'frame': str,  # Frame identifier
            'features': [float],  # Body movement feature vector (179-dimensional)
        }
    ],

    'harmful_utterance_id': [int],  # List of utterance IDs identified as harmful

    'speaker_bbox': [  # Speaker bounding boxes
        {
            'frame_id': int,  # Frame identifier
            'bbox': [int],  # [x_top, y_top, x_bottom, y_bottom]
        }
    ],
}

```

### Data Instances

See above

### Data Fields

See above

### Data Splits

Data splits can be accessed as:
```python
from datasets import load_dataset
train_dataset = load_dataset("winston1214/VENUS-1K", split = "train")
test_dataset = load_dataset("winston1214/VENUS-1K", split = "test")
```

### Curation Rationale

Full details are in the paper.

### Source Data
We retrieve natural videos from YouTube and annotate the FLAME and SMPL-X parameters from EMOCAv2 and OSX.

### Initial Data Collection
Full details are in the paper.

### Annotations

Full details are in the paper.

### Annotation Process

Full details are in the paper.

### Who are the annotators?

We used an automatic annotation method, and the primary annotator was Youngmin Kim, the first author of the paper.

For any questions regarding the dataset, please contact <a href='winston1214@yonsei.ac.kr'>e-mail</a>

### Considerations for Using the Data

This dataset (VENUS) consists of 3D annotations of human subjects and text extracted from conversations in the videos.
Please note that the dialogues are sourced from online videos and may include informal or culturally nuanced expressions.
Use of this dataset should be done with care, especially in applications involving human-facing interactions.

### Licensing Information

The annotations we provide are licensed under CC-BY-4.0.