Datasets:
Tasks:
Video Classification
Formats:
csv
Languages:
English
Size:
100K - 1M
ArXiv:
Tags:
co-speech gestures
semantic gestures
gesture-recognition
temporal-localization
video-understanding
multimodal-learning
License:
| license: apache-2.0 | |
| annotations_creators: | |
| - expert-generated | |
| - machine-generated | |
| language: | |
| - en | |
| size_categories: | |
| - 100K<n<1M | |
| task_categories: | |
| - video-classification | |
| pretty_name: GRW — Gesture Recognition in-the-Wild | |
| tags: | |
| - co-speech gestures | |
| - semantic gestures | |
| - gesture-recognition | |
| - temporal-localization | |
| - video-understanding | |
| - multimodal-learning | |
| configs: | |
| - config_name: semantic_classification | |
| data_files: | |
| - split: train | |
| path: semantic_classification_train.csv | |
| - split: test | |
| path: semantic_classification_test.csv | |
| - split: test_unseen_words | |
| path: semantic_classification_test_unseen_words.csv | |
| - config_name: recognition_localization | |
| data_files: | |
| - split: train | |
| path: recognition_localization_train.csv | |
| - split: test | |
| path: recognition_localization_test.csv | |
| # GRW: Gesture Recognition in-the-Wild | |
| This dataset is associated with the paper **"Recognizing Co-speech Gestures in-the-Wild"** (ECCV 2026). | |
| <p align="center"> | |
| <a href="https://arxiv.org/abs/2605.31589"><img src="https://img.shields.io/badge/arXiv-2605.31589-b31b1b.svg" alt="arXiv"></a> | |
| <a href="https://www.robots.ox.ac.uk/~vgg/research/grw/"><img src="https://img.shields.io/badge/Project-Page-blue.svg" alt="Project Page"></a> | |
| <a href="https://www.robots.ox.ac.uk/~vgg/research/grw/dataset"><img src="https://img.shields.io/badge/Explore-Dataset-green.svg" alt="Explore the dataset"></a> | |
| <a href="https://github.com/Sindhu-Hegde/grw"><img src="https://img.shields.io/badge/Code-GitHub-black.svg" alt="Code"></a> | |
| <a href="https://opensource.org/licenses/Apache-2.0"><img src="https://img.shields.io/badge/License-Apache_2.0-yellow.svg" alt="License: Apache 2.0"></a> | |
| </p> | |
| <p align="center"> | |
| <img src="https://raw.githubusercontent.com/Sindhu-Hegde/grw/main/assets/teaser.gif" width="600" alt="Semantic co-speech gestures recognized and localized in real-world videos"/> | |
| </p> | |
| Our aim is to recognise and localize **semantic gestures** in real-world videos. These gestures are visually depictive and semantically linked to specific spoken words. We introduce a new large-scale benchmark, **GRW** (Gesture Recognition in-the-Wild), which provides word-level annotations and gesture boundaries for semantic gestures occurring in unconstrained real-world settings. | |
| --- | |
| ## 📋 Table of Contents | |
| - [📚 What is GRW?](#📚-what-is-grw) | |
| - [⚡ Quickstart](#⚡-quickstart) | |
| - [📦 Getting the data](#📦-getting-the-data) | |
| - [📝 Dataset structure](#📝-dataset-structure) | |
| - [Task 1: Semantic classification](#task-1-semantic-classification) | |
| - [Task 2: Gesture recognition & localization](#task-2-gesture-recognition--localization) | |
| - [Data instances](#data-instances) | |
| - [📊 Statistics](#📊-statistics) | |
| - [🏆 Benchmark results](#🏆-benchmark-results) | |
| - [🔖 Citation](#🔖-citation) | |
| - [📧 Contact](#📧-contact) | |
| - [⚖️ License & data usage](#⚖️-license--data-usage) | |
| --- | |
| ## 📚 What is GRW? | |
| GRW is a large-scale video dataset for gesture recognition in the wild, containing over **17,000 semantic gesture clips** spanning **155 gesture words**. Each video clip is **4 seconds** long and features a single speaker performing a co-speech gesture. The dataset covers a wide range of gesture categories, including iconic, deictic, metaphoric, and beat gestures, across diverse speakers, backgrounds, and real-world settings. In addition to word-level speech boundaries, the dataset provides **manually annotated temporal gesture boundaries** for each video clip. | |
| <div align="center"> | |
| | **17K+** | **155** | **3** | | |
| |:--:|:--:|:--:| | |
| | Semantic gesture clips | Word vocabulary | Modalities<br>Video · Speech · Text | | |
| </div> | |
| The dataset supports two tasks: | |
| | # | Task | Given | Predict | | |
| |:--|:--|:--|:--| | |
| | **1** | **Semantic classification** | A candidate video segment and a spoken target word | Whether the segment contains a gesture semantically related to that word | | |
| | **2** | **Gesture recognition & localization** | A gesture video clip | The gestured word, and the temporal gesture boundaries | | |
| > 🎬 **Browse the videos** on the [interactive dataset explorer](https://www.robots.ox.ac.uk/~vgg/research/grw/dataset) before downloading. | |
| --- | |
| ## ⚡ Quickstart | |
| This repository contains the **annotation CSV files**. The videos themselves are sourced from YouTube and obtained separately — see [Getting the data](#📦-getting-the-data). | |
| ```python | |
| from datasets import load_dataset | |
| # Task 1: Semantic classification | |
| sem = load_dataset("sindhuhegde/grw", "semantic_classification") | |
| # splits: "train", "test", "test_unseen_words" | |
| # Task 2: Gesture recognition & localization | |
| rec = load_dataset("sindhuhegde/grw", "recognition_localization") | |
| # splits: "train", "test" | |
| print(rec["test"][0]) | |
| ``` | |
| Or read the CSVs directly with `pandas`: | |
| ```python | |
| import pandas as pd | |
| from huggingface_hub import hf_hub_download | |
| path = hf_hub_download("sindhuhegde/grw", | |
| "recognition_localization_test.csv", | |
| repo_type="dataset") | |
| df = pd.read_csv(path) | |
| ``` | |
| <details> | |
| <summary><b>Files in this repository</b></summary> | |
| | File | Task | Split | Rows | | |
| |:--|:--|:--|--:| | |
| | `semantic_classification_train.csv` | Semantic classification | train | 135,503 | | |
| | `semantic_classification_test.csv` | Semantic classification | test | 4,000 | | |
| | `semantic_classification_test_unseen_words.csv` | Semantic classification | test (unseen words) | 500 | | |
| | `recognition_localization_train.csv` | Recognition & localization | train | 15,340 | | |
| | `recognition_localization_test.csv` | Recognition & localization | test | 2,000 | | |
| </details> | |
| --- | |
| ## 📦 Getting the data | |
| The CSVs give you YouTube IDs, timestamps and speaker bounding boxes. There are **two ways** to obtain the data — pick whichever suits you. | |
| ### Option A — Download pre-extracted SHuBERT features *(recommended)* | |
| Our gesture models take SHuBERT features as input, and we release them directly. **This skips video downloading and preprocessing entirely.** | |
| | Feature set | Download | | |
| |:--|:--| | |
| | Semantic Classification — Train | [Link](https://thor.robots.ox.ac.uk/grw/shubert-features/semantic_classification_train.tar) | | |
| | Semantic Classification — Test | [Link](https://thor.robots.ox.ac.uk/grw/shubert-features/semantic_classification_test.tar) | | |
| | Semantic Classification — Test unseen words | [Link](https://thor.robots.ox.ac.uk/grw/shubert-features/semantic_classification_test_unseen_words.tar) | | |
| | Word Recognition & Localization — Train | [Link](https://thor.robots.ox.ac.uk/grw/shubert-features/recognition_localization_train.tar) | | |
| | Word Recognition & Localization — Test | [Link](https://thor.robots.ox.ac.uk/grw/shubert-features/recognition_localization_test.tar) | | |
| Checksums are available [here](https://thor.robots.ox.ac.uk/grw/shubert-features/SHA512SUMS) and can be verified with `sha512sum -c SHA512SUMS`. | |
| <details> | |
| <summary><b>Folder structure of the extracted features</b></summary> | |
| ```text | |
| shubert_features (path of the extracted shubert features) | |
| ├── semantic_classification | |
| │ ├── split (<train>/<test>/<test_unseen_words>) | |
| │ │ ├── *.npy | |
| ├── recognition_localization | |
| │ ├── split (<train>/<test>) | |
| │ │ ├── *.npy | |
| ``` | |
| </details> | |
| ### Option B — Download and crop the videos yourself | |
| Use the preprocessing scripts in the [code repository](https://github.com/Sindhu-Hegde/grw): | |
| ```bash | |
| git clone https://github.com/Sindhu-Hegde/grw.git | |
| cd grw/preprocess | |
| # Download the videos from YouTube-ids and timestamps | |
| python download_videos.py --input_csv=<csv-file> --result_dir=<raw-video-root> | |
| # Crop the videos with the bounding-box co-ordinates provided in the csv files | |
| python crop_videos.py --input_csv=<csv-file> --video_dir=<raw-video-root> --output_dir=<preprocessed-video-root> | |
| ``` | |
| > ⚠️ **Note:** Due to new YouTube policies, downloading videos (especially for the train sets) might take a long time. If you only need to train or evaluate the gesture models, prefer **Option A**. | |
| <details> | |
| <summary><b>Folder structure after download and pre-processing</b></summary> | |
| ```text | |
| raw_video_root (path of the downloaded raw videos) | |
| ├── *.mp4 (raw uncropped videos) | |
| ``` | |
| ```text | |
| preprocessed_video_root (path of the pre-processed gesture videos) | |
| ├── word folders | |
| │ ├── *.mp4 (extracted person-specific gesture video) | |
| ``` | |
| </details> | |
| --- | |
| ## 📝 Dataset structure | |
| All frame indices are given **at 25 fps**. | |
| ### Task 1: Semantic classification | |
| Video segment annotations for classifying whether a candidate segment contains a gesture that is semantically related to the spoken target word. | |
| `semantic_classification_train.csv` · `semantic_classification_test.csv` · `semantic_classification_test_unseen_words.csv` | |
| | Column | Description | | |
| |:--|:--| | |
| | `fname` | unique video clip identifier | | |
| | `target_word` | gesture word label | | |
| | `speech_start` / `speech_end` | speech boundaries (in frames at 25fps), automatically extracted using WhisperX | | |
| | `context_start` / `context_end` | frame boundaries (at 25fps) of the context window preceding the candidate segment | | |
| | `target_start` / `target_end` | frame boundaries (at 25fps) of the candidate segment being classified | | |
| | `word_form` | surface form of the word as spoken | | |
| | `gesture_label` | `1` if the candidate segment contains a gesture semantically related to the target word, `0` otherwise | | |
| | `source_file` | YouTube video ID | | |
| | `source_video_start` / `source_video_end` | start and end timestamps (in seconds) of the clip within the source video | | |
| | `source_width` / `source_height` | resolution (in pixels) of the source video | | |
| | `num_frames` | total number of frames in the original (long) source video; provided for preprocessing | | |
| | `pad` / `bbox` | padding (in pixels) and bounding box `[x1, y1, x2, y2]` (in pixels) computed on the original source video; used internally to crop the speaker region during preprocessing | | |
| ### Task 2: Gesture recognition & localization | |
| Video segment annotations with manually annotated gesture boundaries, for gesture recognition and temporal localization. | |
| `recognition_localization_train.csv` · `recognition_localization_test.csv` | |
| | Column | Description | | |
| |:--|:--| | |
| | `fname` | unique video clip identifier | | |
| | `target_word` | gesture word label | | |
| | `speech_start` / `speech_end` | speech boundaries (in frames at 25fps), automatically extracted using WhisperX | | |
| | `gesture_start` / `gesture_end` | **manually annotated** gesture boundaries (in frames at 25fps) | | |
| | `word_form` | surface form of the word as spoken | | |
| | `source_file` | YouTube video ID | | |
| | `source_video_start` / `source_video_end` | start and end timestamps (in seconds) of the clip within the source video | | |
| | `source_width` / `source_height` | resolution (in pixels) of the source video | | |
| | `num_frames` | total number of frames in the original (long) source video; provided for preprocessing | | |
| | `pad` / `bbox` | padding (in pixels) and bounding box `[x1, y1, x2, y2]` (in pixels) computed on the original source video; used internally to crop the speaker region during preprocessing | | |
| > 🔒 For **both** tasks, the train and test sets have **disjoint videos**. | |
| ### Data instances | |
| <details open> | |
| <summary><b>Recognition & localization</b></summary> | |
| ```json | |
| { | |
| "fname": "entire/00000", | |
| "target_word": "entire", | |
| "speech_start": 35, | |
| "speech_end": 44, | |
| "gesture_start": 25, | |
| "gesture_end": 67, | |
| "word_form": "entire", | |
| "source_file": "7IwTua4AaRY", | |
| "source_video_start": 58.36, | |
| "source_video_end": 62.36, | |
| "source_width": 1920, | |
| "source_height": 1080, | |
| "num_frames": 282, | |
| "pad": 454, | |
| "bbox": "[1259, 521, 2168, 1430]" | |
| } | |
| ``` | |
| </details> | |
| <details> | |
| <summary><b>Semantic classification (a <code>gesture_label = 0</code> example)</b></summary> | |
| ```json | |
| { | |
| "fname": "look/00012", | |
| "target_word": "look", | |
| "speech_start": 197, | |
| "speech_end": 201, | |
| "context_start": 0, | |
| "context_end": 149, | |
| "target_start": 150, | |
| "target_end": 249, | |
| "word_form": "look", | |
| "gesture_label": 0.0, | |
| "source_file": "ogCJrrvgais", | |
| "source_video_start": 1054.48, | |
| "source_video_end": 1058.48, | |
| "source_width": 1280, | |
| "source_height": 720, | |
| "num_frames": 1725, | |
| "pad": 426, | |
| "bbox": "[727, 384, 1579, 1236]" | |
| } | |
| ``` | |
| </details> | |
| Use the [dataset viewer](https://huggingface.co/datasets/sindhuhegde/grw/viewer) to explore more examples. | |
| --- | |
| ## 📊 Statistics | |
| Every clip is 4 seconds long. All counts below are computed directly from the released CSV files. | |
| ### Task 1: Semantic classification | |
| | Split | # Rows | # Words | # Source videos | Gestured (`1`) | Not gestured (`0`) | | |
| |:--|--:|--:|--:|--:|--:| | |
| | `train` | 135,503 | 155 | 34,132 | 15,340 | 120,163 | | |
| | `test` | 4,000 | 100 | 3,297 | 2,000 | 2,000 | | |
| | `test_unseen_words` | 500 | 10 | 468 | 201 | 299 | | |
| The `test_unseen_words` split evaluates generalization to words **never seen during training** — its 10 words have zero overlap with the 155 training words: `crawl`, `direct`, `enlarge`, `great`, `ingest`, `loads`, `proximity`, `rearrange`, `synchronize`, `uniform`. | |
| ### Task 2: Gesture recognition & localization | |
| | Split | # Clips | # Words | # Source videos | Clips per word (median) | | |
| |:--|--:|--:|--:|--:| | |
| | `train` | 15,340 | 155 | 10,407 | 65 | | |
| | `test` | 2,000 | 100 | 1,435 | 14 | | |
| All 100 test words are contained within the 155 training words. In the training set, the annotated gestures last **40 frames on average (≈1.6 s)** out of the 4-second clip. | |
| ### Vocabulary | |
| <details> | |
| <summary><b>The 155 gesture words</b></summary> | |
| ``` | |
| above, absorb, angle, arc, around, ascend, back, balance, barrier, beautiful, begin, | |
| below, big, block, boost, bottom, bounce, branch, break, broad, build, bundle, bye, | |
| call, catch, circle, close, collect, collide, combine, compress, condense, connect, | |
| count, cross, cup, curve, decrease, deep, descend, develop, direction, down, eat, | |
| elevate, embrace, engage, entire, evolve, expand, explode, few, fight, five, flip, | |
| flow, focus, force, four, front, full, gigantic, global, grab, grasp, grow, hashtag, | |
| heavy, height, hello, her, high, hold, horizontal, hug, huge, increase, interaction, | |
| join, knock, large, layer, less, lift, link, little, long, look, loop, lower, many, | |
| merge, mix, move, narrow, no, open, overlap, pause, peak, perfect, pieces, point, | |
| press, process, push, quick, raise, reduce, roll, rotate, round, run, separate, | |
| shake, she, short, shrink, slide, small, specific, spin, spiral, stack, stop, | |
| straight, stretch, strong, switch, three, throw, tie, tight, tilt, tiny, together, | |
| top, track, transform, transition, trap, turn, twist, two, unify, us, various, wait, | |
| walk, wave, whole, wide, wrap, yes, zoom | |
| ``` | |
| The full list is also browsable on the [Word List page](https://www.robots.ox.ac.uk/~vgg/research/grw/dataset/#/wordlist). | |
| </details> | |
| `target_word` is the canonical class label, while `word_form` records how the word was actually spoken — so inflections are preserved (e.g. `quick` → `quickly`, `wave` → `waving`, `rotate` → `rotating`). The training set contains 381 distinct `target_word` / `word_form` pairs. | |
| --- | |
| ## 🏆 Benchmark results | |
| Results of our models on the GRW test sets, reproducible with the [evaluation scripts](https://github.com/Sindhu-Hegde/grw#-evaluation). | |
| <div align="center"> | |
| *Semantic gesture classification on the GRW test set* | |
| | Accuracy | Precision | Recall | High-confidence Accuracy | | |
| |:--:|:--:|:--:|:--:| | |
| | **75.83** | **79.91** | **69.00** | **93.20** | | |
| *Word recognition and localization on the GRW test set* | |
| | Acc@1 | Acc@5 | Acc@10 | mIoU | | |
| |:--:|:--:|:--:|:--:| | |
| | **18.35** | **37.30** | **51.70** | **0.67** | | |
| </div> | |
| --- | |
| ## 🔖 Citation | |
| If you find this dataset helpful, please consider starring ⭐ the [repository](https://github.com/Sindhu-Hegde/grw) and citing our work. | |
| ```bibtex | |
| @inproceedings{hegde_eccv_2026, | |
| title={Recognizing Co-Speech Gestures in-the-Wild}, | |
| author={Hegde, Sindhu and Prajwal, K R and Zisserman, Andrew}, | |
| booktitle={European Conference on Computer Vision (ECCV)}, | |
| year={2026} | |
| } | |
| ``` | |
| --- | |
| ## 📧 Contact | |
| For questions about the dataset, access requests, or collaboration inquiries, please email **sindhu@robots.ox.ac.uk**. | |
| | Author | Affiliation | | |
| |:--|:--| | |
| | [Sindhu Hegde](https://sindhu-hegde.github.io/) | University of Oxford | | |
| | [K R Prajwal](https://www.robots.ox.ac.uk/~prajwal/) | University of Oxford | | |
| | [Andrew Zisserman](https://scholar.google.com/citations?hl=en&user=UZ5wscMAAAAJ) | University of Oxford | | |
| Visual Geometry Group (VGG) · Department of Engineering Science · University of Oxford | |
| --- | |
| ## ⚖️ License & data usage | |
| The annotations in this repository are released under the **Apache 2.0** licence. The underlying videos are sourced from YouTube and are not redistributed here — they remain subject to their original terms of use. The dataset is intended for research purposes. | |