---
license: cc-by-nc-sa-4.0
language:
- en
pretty_name: "InterAct Dataset: Two-Person Multimodal"
tags:
- motion-capture
- motion-generation
- motion-models
- social-robotics
- computer-vision
size_categories:
- 1K<n<10K
---
# InterAct Dataset

InterAct is a multi-modal two-person interaction dataset for research in human motion, facial expressions, and speech. For details, please refer to [our webpage](https://hku-cg.github.io/interact/).

## Quick Start

A Quick Start Jupyter notebook is provided at `quickstart.ipynb`. It covers examples for:

1. Querying the scenario and actor databases
2. Finding actor pairs for a recording session
3. Loading performance data (BVH, face parameters, audio)
4. Visualizing face blendshapes over time
5. Loading both actors in a two-person interaction

## Repository Structure

### Database Files

#### `scenarios.db`
SQLite database containing scenario metadata with the following tables:

- **scenarios**: Contains scenario definitions
  - `id` (INTEGER): Scenario ID (used in filenames)
  - `relationship_id` (INTEGER): FK to relationships table
  - `primary_emotion_id` (INTEGER): FK to emotions table
  - `character_setup` (TEXT): Character context description
  - `scenario` (TEXT): Scenario description

- **relationships**: Relationship types between actors (e.g., "architect / contractor", "boss / subordinate")
  - `id` (INTEGER): Relationship ID
  - `name` (VARCHAR): Relationship description

- **emotions**: Primary emotion categories (e.g., "admiration", "anger", "amusement")
  - `id` (INTEGER): Emotion ID
  - `name` (VARCHAR): Emotion name

#### `actors.db`
SQLite database containing actor and session information:

- **actors**: Actor metadata
  - `actor_id` (TEXT): Three-digit actor ID (e.g., "001", "002")
  - `gender` (TEXT): "male" or "female"

- **sessions**: Recording session information
  - `date` (TEXT): Session date in YYYYMMDD format
  - `male_id` (TEXT): Actor ID of the male participant
  - `female_id` (TEXT): Actor ID of the female participant

---

### Data Directories

Motion and facial data are provided here at **30 fps**. The performance data files follow this naming convention:
```
<date>_<actor_id>_<scenario_id>.<extension>
```
Example: `20231119_001_051.bvh` = recorded on 2023-11-19, actor 001, scenario 51

#### `bvhs/`
BVH motion capture files of the performances.

#### `bvhs_retarget/`
Retargeted BVH files for use in `body_to_render.blend`.

#### `face_ict/`
Facial blendshape parameters in ICT-FaceKit format (shape: `(N, 55)`). Suitable for training models and rendering with `face_ict_to_render.blend`.

#### `face_arkit/`
Facial blendshape parameters in ARKit format (shape: `(N, 51)`). Used in `body_to_render.blend` for full body visualization.

#### `face_ict_templates/`
Base mesh templates in ICT-FaceKit topology, named by actor ID (e.g., `001.obj`). Useful for training models.

#### `wav/`
Audio recordings from each actor in each performance.

#### `body_renders/`
Pre-rendered full-body visualizations (body + face + audio) as MP4 videos. These files use a different naming convention since they contain both actors:
```
<date>_<scenario_id>.mp4
```
Example: `20231119_051.mp4` = scenario 51 recorded on 2023-11-19

#### `lip_acc/`
Additional 1-hour facial dataset with attention to accuracy of lip shapes and pronunciation. Only one actor (006) was captured in this dataset, and the `scenario_id` of these files correspond to the order of the sentences in `lip_acc_sentences.txt`. Useful for fine-tuning.

---

### Scripts (`scripts/`)

#### Blender Files

- **`body_to_render.blend`**: Blender project for rendering full-body (face+body) visualizations. Contains pre-configured character rigs mapped to actor IDs. The "composite scene in dataset" script reads job files, composites both actors with BVH body motion from `bvhs_retarget/` and ARKit face blendshapes from `face_arkit/`. The "render all scenes" script renders MKV videos to `body_renders_noaudio/`.

- **`face_ict_to_render.blend`**: Blender project for rendering face-only visualizations using ICT-FaceKit topology. Contains pre-configured actor mesh scenes (`mesh-001`, `mesh-002`, etc.) and a "composite scenes and render" script that reads job files, loads blendshape animations from `face_ict/`, and renders 1080x1080 PNG sequences at 30fps using EEVEE. Output goes to `face_renders_noaudio/`.

#### Conversion Scripts

- **`face_ict_to_arkit.py`**: Converts ICT-FaceKit blendshape parameters (55 blendshapes) to ARKit format (51 blendshapes). Merges certain blendshape pairs and removes unused indices.

- **`face_ict_to_vertices.py`**: Converts ICT blendshape parameters to vertex sequences using the blendshape basis matrix. Outputs per-frame vertex positions as numpy arrays with shape `(N, V*3)`, where coordinates are packed contiguously per vertex: `[v1x, v1y, v1z, v2x, v2y, v2z, ...]`.

#### Render Utilities

- **`render_add_audio.py`**: Combines rendered video with audio tracks. Supports both face renders (single actor) and body renders (mixed audio from both actors).

#### Data Files

- **`blendshape_ict.npy`**: ICT-FaceKit blendshape basis matrix used for converting blendshape parameters to vertex offsets, used in `face_ict_to_vertices.py`.

#### Job Files

We recommend using a job file and splitting the rendering into batches, as opposed to rendering all scenarios in one go.

- **`example_body_render_job.txt`**: Example job file listing scenes to render in body format (`<date>_<scenario_id>`).
- **`example_face_render_job.txt`**: Example job file listing scenes to render in face format (`<date>_<actor_id>_<scenario_id>`).

## Errata

- The face files for `20240126_006_034` is unavailable due to a conversion issue. When rendering the scene in `body_to_render.blend`, the female face blendshape animations are not applied.

## Acknowledgements

`body_to_render.blend` is based on the visualization Blender project kindly provided by the [BEAT dataset](https://pantomatrix.github.io/BEAT/) authors.

If you used InterAct as part of your research, please cite as following:

```bibtex
@article{ho2025interact,
  title={InterAct: A Large-Scale Dataset of Dynamic, Expressive and Interactive Activities between Two People in Daily Scenarios},
  author={Ho, Leo and Huang, Yinghao and Qin, Dafei and Shi, Mingyi and Tse, Wangpok and Liu, Wei and Yamagishi, Junichi and Komura, Taku},
  journal={Proceedings of the ACM on Computer Graphics and Interactive Techniques},
  volume={8},
  number={4},
  pages={1--27},
  year={2025},
  publisher={ACM New York, NY},
  doi={10.1145/3747871}
}
```