Initial commit

Co-authored-by: rajjanardhan00 <rajjanardhan00@users.noreply.huggingface.co>
Co-authored-by: JasonZheng20 <JasonZheng20@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

LICENSE

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README.md

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+---
+license: cc-by-nc-4.0
+configs:
+- config_name: improvised
+  data_files:
+  - split: dev
+    path:
+    - improvised/dev/**/*
+  - split: test
+    path:
+    - improvised/test/**/*
+  - split: train
+    path:
+    - improvised/train/**/*
+- config_name: naturalistic
+  data_files:
+  - split: dev
+    path:
+    - naturalistic/dev/**/*
+  - split: test
+    path:
+    - naturalistic/test/**/*
+  - split: train
+    path:
+    - naturalistic/train/**/*
+
+tags:
+- webdataset
+- audio
+- video
+pretty_name: Seamless Interaction
+---
+
+
+<div align="center">
+
+<h1>Seamless Interaction Dataset</h1>
+
+<img src="https://github.com/zyaoj/zhiyuanyaoj.github.io/blob/24666287c3f6dc5efb79389c95a42a38bf78f06a/assets/images/fair/seamless_interaction_banner.gif?raw=true" alt="Seamless Interaction Dataset Banner" width="800px">
+
+**A large-scale multimodal dataset of 4,000+ hours of human interactions for AI research**
+
+
+<table>
+<tr>
+<td align="center">
+<a href="https://ai.meta.com/blog/seamless-interaction-natural-conversational-dynamics/">
+🖼️ Blog
+</a>
+</td>
+<td align="center">
+<a href="https://ai.meta.com/research/seamless-interaction/">
+🌐 Website
+</a>
+</td>
+<td align="center">
+<a href="https://www.aidemos.meta.com/seamless_interaction_dataset">
+🎮 Demo
+</a>
+</td>
+<td align="center">
+<a href="https://github.com/facebookresearch/seamless_interaction">
+📦 GitHub
+</a>
+</td>
+<td align="center">
+<a href="https://ai.meta.com/research/publications/seamless-interaction-dyadic-audiovisual-motion-modeling-and-large-scale-dataset">
+📄 Paper
+</a>
+</td>
+</tr>
+</table>
+
+
+</div>
+
+Human communication involves a complex interplay of verbal and nonverbal signals, essential for conveying meaning and achieving interpersonal goals.
+
+The **Seamless Interaction Dataset** is a large-scale collection of over 4,000 hours of face-to-face interaction footage from more than 4,000 participants in diverse contexts.
+This dataset enables the development of AI technologies that understand human interactions and communication, unlocking breakthroughs in:
+
+- 🤖 Virtual agents and embodied AI
+- 🎭 Natural human-computer interaction
+- 📡 Advanced telepresence experiences
+- 📊 Multimodal content analysis tools
+- 🎬 Animation and synthetic content generation
+
+## 🚀 Quick Start
+
+```bash
+git clone https://github.com/facebookresearch/seamless-interaction
+cd seamless-interaction
+pip install -e .
+streamlit run src/seamless_interaction/app/Welcome.py
+
+# if you use uv
+uv sync
+uv run streamlit run src/seamless_interaction/app/Welcome.py
+```
+
+Explore the dataset with our interactive browser:
+
+**Features:**
+- 🔍 **Hierarchical Navigation**: Browse by Label → Split → Batch → Interaction
+- 🎲 **Random Sampling**: Discover interactions with one-click random selection
+- 📥 **Download Interface**: Download specific batches with size estimation and progress tracking
+- 🎬 **Video Viewer**: Side-by-side participant videos with synchronized playback
+- 📊 **Data Analysis**: Overview statistics and distribution plots
+- 📁 **File Management**: Organize and preview audio, JSON, and NPZ files with expandable dropdowns
+
+### Download Options
+
+We provide comprehensive download methods supporting all research scales and requirements:
+
+| **Scale** | **Size** | **Method** | **Use Case** | **Script** | **Sampling** |
+|-----------|----------|------------|--------------|------------|-------------|
+| 🔍 **Single Example** | ~100MB | S3 | Quick exploration, understanding data structure | [`download_s3.py`](https://github.com/facebookresearch/seamless_interaction/blob/main/scripts/download_s3.py#L10) | Auto-sample from preferred vendors |
+| 👥 **Interaction Pair** | ~200MB | S3 | Study conversational dynamics between participants | [`download_s3.py`](https://github.com/facebookresearch/seamless_interaction/blob/main/scripts/download_s3.py#L34) | Auto-detect conversation pairs |
+| 📂 **Sample Set** | ~1GB | S3/HF | Initial prototyping, algorithm development | [`download_s3.py`](https://github.com/facebookresearch/seamless_interaction/blob/main/scripts/download_s3.py#L66), [`download_hf.py`](https://github.com/facebookresearch/seamless_interaction/blob/main/scripts/download_hf.py#L10) | File selection or archive-based |
+| 🎯 **Session Groups** | ~400MB | S3 | Deep conversational context, session dynamics | [`download_s3.py`](https://github.com/facebookresearch/seamless_interaction/blob/main/scripts/download_s3.py#L100) | Auto-sample rich sessions |
+| 📦 **Single Batch** | ~50GB | HF | Substantial local development, full exploration | [`download_hf.py`](https://github.com/facebookresearch/seamless_interaction/blob/main/scripts/download_hf.py#L24) | WebDataset tarball download |
+| 🗂️ **Multiple Batches** | ~150GB+ | HF | Training datasets, large-scale analysis | [`download_hf.py`](https://github.com/facebookresearch/seamless_interaction/blob/main/scripts/download_hf.py#L38) | WebDataset tarball download |
+| 🎯 **Different Splits** | Variable | HF | Cross-validation (train/dev/test, improvised/naturalistic) | [`download_hf.py`](https://github.com/facebookresearch/seamless_interaction/blob/main/scripts/download_hf.py#L55) | WebDataset tarball download |
+| 🌍 **Whole Dataset** | ~27TB | HF | Complete research dataset, production systems | [`download_hf.py`](https://github.com/facebookresearch/seamless_interaction/blob/main/scripts/download_hf.py#L82) | WebDataset tarball download |
+
+
+### Basic Data Loading (HF + WebDataset)
+
+```python
+from datasets import load_dataset
+
+# configure
+label = "improvised"
+split = "dev"
+batch_idx = 0
+archive_list = [0, 1]
+
+base_url = (
+    f"https://huggingface.co/datasets/facebook/"
+    f"seamless-interaction/resolve/main/{label}/{split}/"
+    "{batch_idx:04d}/{archive_idx:04d}.tar"
+)
+urls = [base_url.format(batch_idx=batch_idx, archive_idx=archive_idx) for archive_idx in archive_list]
+dataset = load_dataset(
+    "webdataset", data_files={split: urls}, split=split, streaming=True
+)
+
+for item in dataset:
+    break
+
+isinstance(item["mp4"], bytes)
+# True
+item["npz"].keys()
+# dict_keys(['boxes_and_keypoints:box', 'boxes_and_keypoints:is_valid_box', 'boxes_and_keypoints:keypoints', 'movement:EmotionArousalToken', 'movement:EmotionValenceToken', 'movement:FAUToken', 'movement:FAUValue', 'movement:alignment_head_rotation', 'movement:alignment_translation', 'movement:emotion_arousal', 'movement:emotion_scores', 'movement:emotion_valence', 'movement:expression', 'movement:frame_latent', 'movement:gaze_encodings', 'movement:head_encodings', 'movement:hypernet_features', 'movement:is_valid', 'smplh:body_pose', 'smplh:global_orient', 'smplh:is_valid', 'smplh:left_hand_pose', 'smplh:right_hand_pose', 'smplh:translation'])
+item["json"].keys()
+# dict_keys(['id', 'metadata:transcript', 'metadata:vad'])
+item["wav"].keys()
+# dict_keys(['path', 'array', 'sampling_rate'])
+```
+
+## 📦 Deep Dive into the Dataset
+
+### Dataset Structure
+
+The Seamless Interaction Dataset is organized into two main categories/labels:
+- **Improvised**: Interactions primarily based on predefined scenarios with guided prompts with at least one professional actor.
+- **Naturalistic**: Prompted conversations that can be carried out by normal people.
+
+```
+seamless_interaction
+├── improvised                # Interactions with guided prompts
+│   ├── dev
+│   │   ├── 1P-IS/            # First-party internal state annotations
+│   │   │   └── V<vendor>_S<session>_I<interaction>_P<participant>.json
+│   │   ├── 1P-R/             # First-party internal state rationale annotations
+│   │   │   └── V<vendor>_S<session>_I<interaction>_P<participant>.json
+│   │   ├── 3P-IS/            # Third-party internal state annotations
+│   │   │   └── V<vendor>_S<session>_I<interaction>_P<participant>.json
+│   │   ├── 3P-R/             # Third-party internal state rationale annotations
+│   │   │   └── V<vendor>_S<session>_I<interaction>_P<participant>.json
+│   │   ├── 3P-V/             # Third-party visual annotation
+│   │   │   └── V<vendor>_S<session>_I<interaction>_P<participant>.json
+│   │   ├── audio/            # Speaker-bleed denoised audio
+│   │   │   └── V<vendor>_S<session>_I<interaction>_P<participant>.wav
+│   │   ├── boxes_and_keypoints/
+│   │   │   ├── box/          # Bounding boxes for each participant
+│   │   │   ├── is_valid_box/ # Whether bounding boxes are valid
+│   │   │   └── keypoints/    # Detected facial/body keypoints
+│   │   ├── movement/         # Quantified Imitator movement features
+│   │   │   ├── emotion_arousal/           # Arousal measures
+│   │   │   ├── emotion_valence/           # Valence measures
+│   │   │   ├── emotion_scores/            # Emotion detection scores
+│   │   │   ├── expression/                # Facial expression parameters
+│   │   │   ├── FAUToken/                  # Facial Action Unit tokens
+│   │   │   ├── FAUValue/                  # Facial Action Unit values
+│   │   │   ├── gaze_encodings/            # Eye gaze direction encodings
+│   │   │   ├── head_encodings/            # Head position/rotation encodings
+│   │   │   ├── frame_latent/              # Per-frame latent representations
+│   │   │   └── is_valid/                  # Validity flags for extracted features
+│   │   ├── smplh/            # SMPL-H body model parameters
+│   │   │   ├── body-pose/    # Body pose parameters
+│   │   │   ├── global_orient/ # Global orientation parameters
+│   │   │   ├── is_valid/     # Valid frames indicators
+│   │   │   ├── left_hand_pose/ # Left hand pose parameters
+│   │   │   ├── right_hand_pose/ # Right hand pose parameters
+│   │   │   └── translation/  # Global translation parameters
+│   │   ├── transcript/       # Time-aligned speech transcription
+│   │   │   └── V<vendor>_S<session>_I<interaction>_P<participant>.jsonl
+│   │   ├── vad/              # Voice activity detection
+│   │   │   └── V<vendor>_S<session>_I<interaction>_P<participant>.jsonl
+│   │   └── video/            # Raw HD video recordings
+│   │       └── V<vendor>_S<session>_I<interaction>_P<participant>.mp4
+│   ├── test/                 # Test split with similar structure
+│   └── train/                # Training split with similar structure
+└── naturalistic/             # Spontaneous conversations
+    ├── dev/                  # Same structure as improvised/dev
+    ├── test/                 # Same structure as improvised/test
+    └── train/                # Same structure as improvised/train
+```
+
+Each file is named according to a consistent convention:
+- `V<vendor_id>`: Collection site/vendor identifier
+- `S<session_id>`: Unique session identifier
+- `I<interaction_id>`: Specific interaction within a session
+- `P<participant_id>`: Individual participant identifier
+
+### Available Modalities and Features
+
+Each interaction in the dataset includes:
+
+| Modality | Description | File Format | Sample Rate |
+|----------|-------------|-------------|-------------|
+| 🎥 Video | High-definition face-to-face footage | MP4 (H.264) | 30/29.97 FPS, 1080p |
+| 🎙️ Audio | Denoised audio with separate channels | WAV | 48kHz, 16-bit |
+| 📝 Transcript | Time-aligned speech transcription | JSONL | - |
+| 🏃 SMPL-H | 3D body model parameters | NPY | 30 Hz |
+| 🧠 Imitator Movement Features | Comprehensive quantified imitator movement data | NPY | 30 Hz |
+| 📊 Annotations | Human-annotated behavioral data | JSON | - |
+| 🔊 VAD | Voice activity detection | JSONL | 100 Hz |
+| 📦 Keypoints | Face and body keypoints | NPY | 30 Hz |
+
+#### Annotation Types
+
+The dataset includes several types of human annotations for rich behavioral analysis:
+
+| Annotation | Hours | Total Annotations | Mean # Tokens |
+|------------|-------------|--------|--------|
+| 1P-IS (1st-party internal state annotations) | 1.1 | 751 | 5.8 |
+| 1P-R (1st-party internal state rationale annotations) | 1.1 | 751 | 10.2 |
+| 3P-IS (3rd-party internal state annotations) | 4.7 | 5132 | 5.2 |
+| 3P-R (3rd-party internal state rationale annotations) | 4.7 | 5132 | 11.3 |
+| 3P-V (3rd-party visual annotation) | 4.7 | 5132 | 14.6 |
+
+Please refer to the [technical report](https://ai.meta.com/research/publications/seamless-interaction-dyadic-audiovisual-motion-modeling-and-large-scale-dataset/) for a more detailed overview of annotations.
+
+#### Movement/Imitator Feature Types
+
+The movement directory contains rich behavioral features (output of the Imitator model):
+
+| Feature | Description |
+|---------|-------------|
+| `emotion_arousal` | Arousal intensity measurements |
+| `emotion_valence` | Valence (positive/negative) measurements |
+| `emotion_scores` | Detected emotion categorical scores |
+| `expression` | Parametric facial expression encodings |
+| `FAUToken`/`FAUValue` | Facial Action Unit tokens and intensity values |
+| `gaze_encodings` | Neural encodings of gaze direction |
+| `head_encodings` | Neural encodings of head position and rotation |
+| `frame_latent` | Per-frame latent representations |
+| `alignment_head_rotation` | Head rotation data for temporal alignment |
+| `alignment_translation` | Translation parameters for temporal alignment |
+| `EmotionArousalToken`/`EmotionValenceToken` | Discretized emotion tokens |
+| `hypernet_features` | Features from hypernetwork processing |
+
+### Dataset Versions
+
+The dataset is organized in self-contained batches for flexible exploration:
+
+| Split | Batches | Size per Batch | Total Size | Description |
+|-------|---------|----------------|------------|-------------|
+| **dev** | 5 | ~50GB | ~500GB | Development/validation set |
+| **test** | 5 | ~50GB | ~500TB | Hold-out test set |
+| **train** | 200+ | ~50GB | ~20TB+ | Full training data |
+
+#### File Format Specifications
+
+Our data is stored in the following formats for optimal usability:
+
+| Format | Description | Usage |
+|--------|-------------|-------|
+| NPZ | NumPy array files | Efficient storage of numerical feature vectors, keypoints, and parameters |
+| JSONL | JSON Lines | Time-aligned annotations with one event per line (e.g., transcripts, VAD) |
+| JSON | JavaScript Object Notation | Structured metadata and annotations with timestamps |
+| MP4 | MPEG-4 Part 14 | High-quality compressed video with H.264 encoding |
+| WAV | Waveform Audio | Uncompressed audio for highest fidelity processing |
+
+## 🧪 Research Applications
+
+The Seamless Interaction Dataset enables research across multiple domains:
+
+### Embodied AI and Virtual Agents
+- Train agents that display natural gestures
+- Model turn-taking dynamics and interaction rhythms
+- Generate contextually appropriate responses to human behavior
+
+### Multimodal Understanding
+- Analyze cross-modal correlations between speech, gesture, and expressions
+- Extract behavioral patterns from large-scale interaction data
+- Develop models to understand social dynamics
+
+### Human-Computer Interaction
+- Design interfaces that respond to subtle human cues
+- Improve telepresence technologies with better behavioral modeling
+- Create more natural conversational agents
+
+### Animation and Content Creation
+- Generate realistic human behaviors for animated characters
+- Synthesize conversational dynamics for virtual production
+- Create training data for digital human technologies
+
+
+## ⚠️ Known Limitations and Noise in Metadata
+
+Given the scale and complexity involved in collecting the Seamless Interaction dataset, there are several known limitations that we will address in our  ongoing work, with improvements planned for in future versions:
+
+### Errors in Human-Based Time-Stamping
+The core unit of the dataset is interactions. An interaction defines the *active time* during which a  participant’s conversation and behavior can be linked to a pair of prompts. We have observed instances of misaligned time-stamps, including:
+- Annotated start/end times may be too early or too late.
+- Occasional misalignment between prompt text and spoken material.
+- Ordering of prompts that may contain off-by-one errors.
+
+Despite our efforts to automatically identify and correct these errors, approximately 10% of the interactions remain affected.
+
+### Time Stamping "Noise" in Moments of Interest (MOI)
+While defining a MOI inherently involves some subjectivity, there are rare instances where:
+- The described behavior only represents a subset of the observed behavior.
+- The duration of the MOI does not fully capture the annotated behavior.
+
+### Incorrect Assignment of Participant IDs
+In rare instances, we have observed:
+- Duplicate participant identifiers being assigned to different individuals.
+- The same individual being mapped to different identifiers.
+
+### Unreleased "Meta Time"
+Currently, the dataset only contains *active time* segments - time in which two participants are actively responding to prompts. *Meta time* refers to the time between *active segments* in which participants are studying their new prompts, taking a break, etc. *Meta time* constitutes hundreds of hours in the raw collection and maybe be explored for future releases.
+
+### Variation in Recording Site Consistency
+This multi-site project contains variation in:
+- Recording quality, including issues like speaker bleed and participants staying in frame.
+- Acting quality in *Improvised* segments.
+- The likelihood of time-stamping errors.
+
+All vendors met our technical requirements; however,there is noticeable variation in production quality across different sites.
+
+## 📄 License & Data Usage Policy
+
+The Seamless Interaction Dataset is licensed under CC-BY-NC 4.0 (Creative Commons Attribution-NonCommercial 4.0 International).
+
+This means you are free to:
+- **Share** — copy and redistribute the material in any medium or format
+- **Adapt** — remix, transform, and build upon the material
+
+Under the following terms:
+- **Attribution** — You must give appropriate credit, provide a link to the license, and indicate if changes were made.
+- **NonCommercial** — You may not use the material for commercial purposes without explicit permission.
+
+
+## 📑 Citation
+
+If you use the Seamless Interaction Dataset in your research, please cite:
+
+
+<details>
+<summary>BibTeX</summary>
+
+```bibtex
+@article{seamless_interaction,
+  title={Seamless Interaction: Dyadic Audiovisual Motion Modeling and Large-Scale Dataset},
+  author={Vasu Agrawal and
+		Akinniyi Akinyemi and
+		Kathryn Alvero and
+		Morteza Behrooz and
+		Julia Buffalini and
+		Fabio Maria Carlucci and
+		Joy Chen and
+		Junming Chen and
+		Zhang Chen and
+		Shiyang Cheng and
+		Praveen Chowdary and
+		Joe Chuang and
+		Antony D'Avirro and
+		Jon Daly and
+		Ning Dong and
+		Mark Duppenthaler and
+		Cynthia Gao and
+		Jeff Girard and
+		Martin Gleize and
+		Sahir Gomez and
+		Hongyu Gong and
+		Srivathsan Govindarajan and
+		Brandon Han and
+		Sen He and
+		Denise Hernandez and
+		Yordan Hristov and
+		Rongjie Huang and
+		Hirofumi Inaguma and
+		Somya Jain and
+		Raj Janardhan and
+		Qingyao Jia and
+		Christopher Klaiber and
+		Dejan Kovachev and
+		Moneish Kumar and
+		Hang Li and
+		Yilei Li and
+		Pavel Litvin and
+		Wei Liu and
+		Guangyao Ma and
+		Jing Ma and
+		Martin Ma and
+		Xutai Ma and
+		Lucas Mantovani and
+		Sagar Miglani and
+		Sreyas Mohan and
+		Louis-Philippe Morency and
+		Evonne Ng and
+		Kam-Woh Ng and
+		Tu Anh Nguyen and
+		Amia Oberai and
+		Benjamin Peloquin and
+		Juan Pino and
+		Jovan Popovic and
+		Omid Poursaeed and
+		Fabian Prada and
+		Alice Rakotoarison and
+		Alexander Richard and
+		Christophe Ropers and
+		Safiyyah Saleem and
+		Vasu Sharma and
+		Alex Shcherbyna and
+		Jia Shen and
+		Jie Shen and
+		Anastasis Stathopoulos and
+		Anna Sun and
+		Paden Tomasello and
+		Tuan Tran and
+		Arina Turkatenko and
+		Bo Wan and
+		Chao Wang and
+		Jeff Wang and
+		Mary Williamson and
+		Carleigh Wood and
+		Tao Xiang and
+		Yilin Yang and
+		Zhiyuan Yao and
+		Chen Zhang and
+		Jiemin Zhang and
+		Xinyue Zhang and
+		Jason Zheng and
+		Pavlo Zhyzheria and
+		Jan Zikes and
+		Michael Zollhoefer
+  },
+  url={https://ai.meta.com/research/publications/seamless-interaction-dyadic-audiovisual-motion-modeling-and-large-scale-dataset/},
+  year={2025}
+}
+```
+</details>
+
+## 🙏 Acknowledgments
+
+This project was made possible thanks to contributions from:
+
+- The thousands of participants who provided interaction data
+- Our dedicated annotation and QA team
+- Research collaborators from multiple institutions
+- FAIR (Fundamental AI Research)
+- The open-source community for valuable tools and libraries
+- Our data collection partners across multiple sites
+- Meta Reality Labs for supporting this research initiative

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