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README.md

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+---
+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}
+}
+```

quickstart.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# InterAct Dataset - Quick Start\n",
+    "\n",
+    "This notebook demonstrates how to load and explore the InterAct dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sqlite3\n",
+    "import numpy as np\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Loading the Databases\n",
+    "\n",
+    "The dataset includes two SQLite databases:\n",
+    "- `scenarios.db` - scenario metadata (relationships, emotions, descriptions)\n",
+    "- `actors.db` - actor info and recording sessions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Connect to databases\n",
+    "scenarios_db = sqlite3.connect('scenarios.db')\n",
+    "actors_db = sqlite3.connect('actors.db')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# View available relationships\n",
+    "relationships = scenarios_db.execute('SELECT * FROM relationships').fetchall()\n",
+    "print(f\"Total relationships: {len(relationships)}\")\n",
+    "print(\"Sample relationships:\")\n",
+    "for r in relationships[:5]:\n",
+    "    print(f\"  {r[0]}: {r[1]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# View available emotions\n",
+    "emotions = scenarios_db.execute('SELECT * FROM emotions').fetchall()\n",
+    "print(f\"Total emotions: {len(emotions)}\")\n",
+    "print(\"Sample emotions:\")\n",
+    "for e in emotions[:5]:\n",
+    "    print(f\"  {e[0]}: {e[1]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Query scenarios by emotion (e.g., find all \"anger\" scenarios)\n",
+    "anger_scenarios = scenarios_db.execute('''\n",
+    "    SELECT s.id, r.name, e.name, s.scenario \n",
+    "    FROM scenarios s\n",
+    "    JOIN relationships r ON s.relationship_id = r.id\n",
+    "    JOIN emotions e ON s.primary_emotion_id = e.id\n",
+    "    WHERE e.name = 'anger'\n",
+    "    LIMIT 3\n",
+    "''').fetchall()\n",
+    "\n",
+    "print(\"Scenarios with 'anger' emotion:\")\n",
+    "for s in anger_scenarios:\n",
+    "    print(f\"\\nScenario {s[0]} ({s[1]} / {s[2]}):\")\n",
+    "    print(f\"  {s[3][:100]}...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Finding Actor Pairs\n",
+    "\n",
+    "Each recording session has one male and one female actor. The `sessions` table maps dates to actor pairs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# View all sessions\n",
+    "sessions = actors_db.execute('SELECT * FROM sessions').fetchall()\n",
+    "print(\"Recording sessions:\")\n",
+    "print(\"Date       | Male | Female\")\n",
+    "print(\"-\" * 28)\n",
+    "for s in sessions:\n",
+    "    print(f\"{s[0]}  | {s[1]}  | {s[2]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# View all actors\n",
+    "actors = actors_db.execute('SELECT * FROM actors').fetchall()\n",
+    "print(\"Actors:\")\n",
+    "for a in actors:\n",
+    "    print(f\"  {a[0]}: {a[1]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_actor_pair(date):\n",
+    "    \"\"\"Get the male and female actor IDs for a given recording date.\"\"\"\n",
+    "    result = actors_db.execute(\n",
+    "        'SELECT male_id, female_id FROM sessions WHERE date = ?', \n",
+    "        (date,)\n",
+    "    ).fetchone()\n",
+    "    return result\n",
+    "\n",
+    "# Example: get actors for a specific date\n",
+    "date = '20231119'\n",
+    "male_id, female_id = get_actor_pair(date)\n",
+    "print(f\"Session {date}: male={male_id}, female={female_id}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Loading Performance Data\n",
+    "\n",
+    "Performance files follow the naming convention: `<date>_<actor_id>_<scenario_id>.<ext>`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example performance\n",
+    "date = '20231119'\n",
+    "actor_id = '001'\n",
+    "scenario_id = '051'\n",
+    "\n",
+    "# File paths\n",
+    "bvh_path = f'bvhs/{date}_{actor_id}_{scenario_id}.bvh'\n",
+    "face_ict_path = f'face_ict/{date}_{actor_id}_{scenario_id}.npy'\n",
+    "face_arkit_path = f'face_arkit/{date}_{actor_id}_{scenario_id}.npy'\n",
+    "wav_path = f'wav/{date}_{actor_id}_{scenario_id}.wav'\n",
+    "\n",
+    "print(f\"BVH: {bvh_path}\")\n",
+    "print(f\"Face ICT: {face_ict_path}\")\n",
+    "print(f\"Face ARKit: {face_arkit_path}\")\n",
+    "print(f\"Audio: {wav_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load face blendshape parameters\n",
+    "face_ict = np.load(face_ict_path)\n",
+    "face_arkit = np.load(face_arkit_path)\n",
+    "\n",
+    "print(f\"Face ICT shape: {face_ict.shape}\")    # (N, 55) - N frames, 55 ICT blendshapes\n",
+    "print(f\"Face ARKit shape: {face_arkit.shape}\")  # (N, 51) - N frames, 51 ARKit blendshapes\n",
+    "\n",
+    "n_frames = face_ict.shape[0]\n",
+    "duration_sec = n_frames / 30  # 30 fps\n",
+    "print(f\"\\nFrames: {n_frames}\")\n",
+    "print(f\"Duration: {duration_sec:.1f} seconds\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "## 4. Loading Both Actors in an Interaction\n\nFor two-person interaction research, load data from both actors in a scene."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": "def load_interaction(date, scenario_id):\n    \"\"\"Load face and audio data for both actors in an interaction.\"\"\"\n    male_id, female_id = get_actor_pair(date)\n    \n    data = {}\n    for actor_id, role in [(male_id, 'male'), (female_id, 'female')]:\n        prefix = f'{date}_{actor_id}_{scenario_id}'\n        data[role] = {\n            'actor_id': actor_id,\n            'face_ict': np.load(f'face_ict/{prefix}.npy'),\n            'face_arkit': np.load(f'face_arkit/{prefix}.npy'),\n            'wav_path': f'wav/{prefix}.wav',\n            'bvh_path': f'bvhs/{prefix}.bvh',\n        }\n    \n    return data\n\n# Load an interaction\ninteraction = load_interaction('20231119', '051')\n\nprint(\"Male actor:\", interaction['male']['actor_id'])\nprint(f\"  Face shape: {interaction['male']['face_ict'].shape}\")\nprint(\"\\nFemale actor:\", interaction['female']['actor_id'])\nprint(f\"  Face shape: {interaction['female']['face_ict'].shape}\")"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "## 5. Basic Visualization\n\nPlot face blendshape values over time."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": "import matplotlib.pyplot as plt\n\n# Plot jawOpen blendshape over time\ntime = np.arange(n_frames) / 30  # Convert to seconds\n\nfig, ax = plt.subplots(figsize=(12, 3))\n\n# ARKit blendshape index (see body_to_render.blend for full list)\nax.plot(time, face_arkit[:, 24])  # 24 = jawOpen\nax.set_ylabel('jawOpen')\nax.set_ylim(0, 1)\nax.set_xlabel('Time (seconds)')\nax.set_title(f'Face Blendshape: {date}_{actor_id}_{scenario_id}')\n\nplt.tight_layout()\nplt.show()"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": "# Compare jaw movement between both actors\nfig, ax = plt.subplots(figsize=(12, 4))\n\nn_frames = interaction['male']['face_arkit'].shape[0]\ntime = np.arange(n_frames) / 30\n\nax.plot(time, interaction['male']['face_arkit'][:, 24], label='Male jawOpen', alpha=0.7)\nax.plot(time, interaction['female']['face_arkit'][:, 24], label='Female jawOpen', alpha=0.7)\n\nax.set_xlabel('Time (seconds)')\nax.set_ylabel('jawOpen')\nax.legend()\nax.set_title('Jaw Movement Comparison - Two-Person Interaction')\nplt.tight_layout()\nplt.show()"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clean up\n",
+    "scenarios_db.close()\n",
+    "actors_db.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

requirements.txt

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+numpy
+matplotlib

scenarios.db

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8c785bc85833127894f33b6c2b6b0855d4230a79cb2d4c099fddf21a6445fcd4
+size 294912