SEMANTIX is a human-annotated evaluation set for **semantic co-speech gesture understanding**. It contains **878 semantic gestures**: 778 from 10 TED talks and 100 from the BEAT2 motion-capture dataset.
Each gesture comes with the speech it accompanies and an expert description of two things:
- **Physical form:** handedness, hand shape, orientation, position and trajectory.
- **Communicative intent:** what the gesture conveys in context.
Use it to evaluate gesture description and text–gesture retrieval with our [code](https://github.com/aboots/Semantic_Motion_Anchors).
**Example** (`ted_1010`, from Wendy Suzuki's talk):
| Field | Value |
|---|---|
| Transcript | to be better in me. |
| Gesture words | in me |
| Physical form | Both hands, with index fingers and thumbs extended while other fingers are closed, point inward toward the body at chest level. |
| Communicative intent | refer to self experience |
| Intent category | Self-reference |
| Watch | [On TED's YouTube channel, at 5:41](https://www.youtube.com/watch?v=BHY0FxzoKZE&t=341s) |
## Dataset configurations
| Configuration | Examples | Contents |
|---|---:|---|
| `all` (default) | 878 | Fields shared by both sources |
| `ted` | 778 | TED annotations, timestamps and YouTube links that open at each gesture |
| `beat2` | 100 | BEAT2 annotations, frame indices and avatar videos |
| `ted_talks` | 10 | Speaker, title, ted.com page and YouTube upload of each TED talk |
| `beat2_anchors` | 17,106 | Generated anchors of all BEAT2 training, validation and test gestures in the paper (see [Add-on](#add-on-semantic-motion-anchors)) |
The four SEMANTIX configurations have a single `test` split. The `beat2_anchors` add-on loads as one `train` split that contains all three experimental splits; filter by its `split` column.
All SEMANTIX rows have `id`, `transcript`, `gesture_words` (the words spoken during the gesture), `gold_description`, `start_sec` and `end_sec`. On top of that:
- **TED rows** add `physical_form`, `communicative_intent`, one of 15 `intent_category` labels, `youtube_url`/`youtube_embed_url`, and `link_check` (see [Videos](#videos)).
- **BEAT2 rows** add the BEAT2 sequence, frame indices and an avatar `video_file`. Their description is a single sentence that ends with the intent.
## Load
```bash
pip install datasets huggingface_hub
```
```python
from datasets import load_dataset
semantix = load_dataset("aboots/SEMANTIX", split="test")
ted = load_dataset("aboots/SEMANTIX", "ted", split="test")
beat2 = load_dataset("aboots/SEMANTIX", "beat2", split="test")
example = ted[0]
print(example["transcript"], "|", example["gesture_words"])
print(example["physical_form"], "->", example["communicative_intent"])
```
## Videos
**TED.** SEMANTIX was annotated on local copies of the TED talks. TED's usage policy does not allow sharing clips of its talks, so we do not host TED video. Each row links to the same talk on TED's official YouTube channel instead:
- `youtube_url` opens the talk at the gesture.
- `youtube_embed_url` requests playback covering the gesture interval, rounded outwards to whole seconds:
```html
```
**The annotations and timestamps are unchanged.** We compared the YouTube uploads with our local copies: they have the same length, and their frames align across each talk.
We then checked every link with word-level speech recognition of the talks:
- **751 of the 778 rows:** speech recognition detects every content word of the gesture within 0.5 s of the linked interval.
- **17 more:** within 2 s.
- **3 rows:** the words are 2.2–2.4 s away.
- **2 rows:** the words are 5–8 s away; these are flagged.
- **5 rows:** recognition missed a word (a spelling or recognition variant), and TED's own transcript has it at the linked time.
Each row's result is in the `link_check` column. [VALIDATION.md](VALIDATION.md) lists word timestamps for every row outside the interval. `start_sec` and `end_sec` hold the exact times.
**BEAT2.** Each row links to a 640×480, 30 fps render of the motion on an SMPL-X avatar, with the speaker's audio. The render covers the gesture with up to 5 s of context on each side.
```python
from huggingface_hub import hf_hub_download
video = hf_hub_download("aboots/SEMANTIX", beat2[0]["video_file"], repo_type="dataset")
```
For the 3D motion, slice the [BEAT2](https://huggingface.co/datasets/H-Liu1997/BEAT2) SMPL-X sequence with the end-exclusive, 30 fps frame indices:
```python
import numpy as np
row = beat2[0]
seq = np.load(f"beat_english_v2.0.0/smplxflame_30/{row['beat2_sequence']}.npz", allow_pickle=True)
poses = seq["poses"][row["start_frame"]:row["end_frame"]]
```
## Sources and data notes
- **TED:** the gestures come from the annotated gesture regions of Ram et al. (UIST 2025) in 10 TED Expressive talks. TED transcripts are Whisper ASR output.
- **BEAT2:** the gestures come from BEAT2 (Liu et al., CVPR 2024).
- **Annotation:** descriptions follow the gesture annotation schema of Kipp (2005).
- **Review:** a second expert reviewed the first 231 TED annotations. For those rows, `reviewed_description` holds the reviewed text, which is the reference used in the paper's anchor-quality evaluation.
- **Shared form text:** within a gesture-lexicon entry (`gesture_lexicon_id`), TED clips share the same physical-form text.
- **Known issues** are marked in the `flags` column, and annotation text is otherwise released as written.
[VALIDATION.md](VALIDATION.md) lists the flags, text clean-up, checksums and link checks.
## Add-on: semantic motion anchors
SEMANTIX is the human-annotated set above. The `addons/` folder holds the inputs of the retrieval experiments in the paper: machine-generated anchors for BEAT2 and the TED evaluation texts. It is not part of the gold annotations and is only needed to reproduce those experiments with our [code](https://github.com/aboots/Semantic_Motion_Anchors).
| File | Rows | Contents |
|---|---:|---|
| `addons/beat2_anchors.csv` (config `beat2_anchors`) | 17,106 | Generated semantic motion anchor of every BEAT2 English deictic, iconic and metaphoric gesture, its physical-form (`physical_form`) and communicative-intent (`communicative_intent`) parts, an LLM-assigned intent category (`intent_category`, one of the 15 SEMANTIX categories, not human-verified), the clip's transcript window and its train/val/test split in the paper |
| `addons/beat2_motion_narratives.jsonl` | 17,106 | Verbalized motion narrative of each gesture: the body and hand description of every 8-frame motion token, which the anchor-generation prompt receives with the transcript |
| `addons/ted_eval.csv` | 778 | The SEMANTIX TED gestures with the transcript query (`query_text`) and the human-written gesture-lexicon description (`lexicon_description`, from Ram et al.) used to represent TED gestures in the paper's TED and cross-dataset evaluations |
```python
anchors = load_dataset("aboots/SEMANTIX", "beat2_anchors", split="train")
train_anchors = anchors.filter(lambda row: row["split"] == "train")
```
- **Generation:** anchors were generated with GPT-5.4 from the motion narrative and the transcript, and split into physical form and intent with Qwen3-8B. Two anchors could not be split and have empty parts. Four anchors contain a stray `;"` left by a spreadsheet export; they are kept exactly as used for training.
- **Clips:** `clip` matches the clip files written by the code's BEAT2 preparation script (`---g------