language: en
task_categories:
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ELLSA 2 — Data Selection and Dialogue Gesture Benchmark
ELLSA 2 studies coordinated speech, gestures, and facial expressions in full-duplex conversation. Its data pipeline selects informative speaking/listening motion, prepares facial supervision, and evaluates communicative behavior using human-verified examples.
This dataset releases data selection/preparation and the dialogue test set. Model training, inference and evaluation implementations are in the sibling code package.
Contents
| Path | Contents |
|---|---|
testset/ |
ELLSA 2 dialogue gesture benchmark, 67 duplex and 71 semantic clips |
manifests/train_all_resolved.json |
Full source pool: 121,054 aligned target/partner recordings, with portable paths |
manifests/train_selected81.json |
Selected inventory of 1,318 recordings from 81 speakers |
manifests/selected_speakers.json |
Selected speaker IDs |
manifests/speaker_quality.csv |
Speaker-level quality statistics |
selection_protocol.json |
Original normalization, thresholds, and selection rules |
preprocess/ |
Scripts for speaker selection and facial extraction |
Each benchmark clip includes aligned target/partner audio, 600-frame SMPL-H and facial ground truth, a separate 612-frame nonoverlapping style clip, transcripts/VAD and reference video. The style pose in testset/style/ is paired with a raw 108D FLAME reference in testset/style_flame/; the manifest and prepared inference jobs include both. Keep the packages as sibling data/, code/, and checkpoints/ directories, or use ELLSA2_DATA_ROOT and ELLSA2_CHECKPOINT_ROOT when running the code package.
Setup
All commands below run from this data directory. Speaker selection can run without the ELLSA2 model package or PyTorch:
python -m pip install -r requirements.txt
KaoLRM extraction uses its own GPU environment. RVQ token caching uses the model environment described in the code guide.
Use ELLSA2 dialogue gesture benchmark
Create inference and rendering jobs:
python preprocess/prepare_testset.py --output-dir outputs/test
# Select one included clip:
python preprocess/prepare_testset.py --test-id fro_00038 --output-dir outputs/example
To run the Standalone generator, switch to the code directory:
cd ../code
python infer.py --model-path ../checkpoints/standalone_generator --test-id fro_00038 --output-dir outputs/example
Prepare the selected training inventory
The included manifests/train_selected81.json is the training input: 1,318 recordings from exactly the 81 IDs in manifests/selected_speakers.json. Each row contains pose, target audio, partner audio, transcript paths and one raw facial-label field, flame_file.
Download the original Seamless Interaction dataset using its official download guide, including person videos (.mp4), SMPL-H poses (.npz), audio and transcripts. Preserve its naturalistic/train/ and improvised/train/ hierarchy. The original dataset has no FLAME files. Extract the raw facial labels below before creating training links. Speaker selection has already been performed for this inventory; the separate speaker-selection workflow is only needed to replay or change that selection.
The manifest expects 16 kHz mono <recording>_16k.wav files for both participants. Convert the selected original WAVs before linking them; existing outputs are retained:
conda install -c conda-forge 'ffmpeg>=6=gpl_*' --freeze-installed -y
python preprocess/prepare_audio.py --dataset-root /path/to/seamless_interaction
Extract raw facial labels
The parameter-only KaoLRM entry point estimates facial labels from source-person video crops on the 30 Hz body timeline. Create a separate extraction environment:
conda create -n ellsa2_kaolrm python=3.12 pip -y
conda activate ellsa2_kaolrm
python -m pip install -r preprocess/requirements-kaolrm.txt
Resolve the selected inventory against your download, then extract labels.
python preprocess/resolve_paths.py \
--input manifests/train_selected81.json \
--dataset-root /path/to/seamless_interaction \
--output outputs/extraction_input.json
python preprocess/extract_kaolrm.py \
--input outputs/extraction_input.json \
--checkpoint ../checkpoints/external_assets/kaolrm-mono \
--output-dir outputs/flame_raw \
--output-manifest outputs/train_raw.json \
--device cuda:0
The extractor writes one <pose_stem>_flame.npz per recording into outputs/flame_raw/, and an absolute-path training manifest into outputs/train_raw.json. Use --max-videos 2 --max-frames 96 for a small check, with a separate output directory.
Link source data and extracted labels
After the complete extraction finishes:
python preprocess/link_training_data.py \
--dataset-root /path/to/seamless_interaction \
--flame-root outputs/flame_raw
This creates source/seamless_interaction/ under the data package. Audio, poses and transcripts link to the downloaded dataset; flame_file links to the newly extracted flat output directory.
Training can now use the included manifests/train_selected81.json unchanged, as described in the training guide.
Speaker selection
The included manifests/train_all_resolved.json supplies the 121,054-recording source pool used for the speaker quality statistics. Its paths are portable data/source/seamless_interaction/... references; --dataset-root maps them to your downloaded files.
1. Score 60-second windows
python preprocess/build_stageA_quality_data.py \
--input-json manifests/train_all_resolved.json \
--dataset-root /path/to/seamless_interaction \
--output-json outputs/train_quality.json \
--output-windows-jsonl outputs/quality_windows.jsonl \
--output-speaker-csv outputs/speaker_quality_new.csv \
--target-sec 60 --stride-sec 30 --num-workers 4
The default scale uses source-pool quantiles frozen in selection_protocol.json. Use --recompute-normalization only when fitting a replacement full-pool scale. If you already have an absolute-path train_all_resolved.json, pass it directly and omit --dataset-root.
| Quality component | Weight |
|---|---|
| Motion validity | 0.12 |
| Smoothness | 0.10 |
| Natural range | 0.10 |
| Motion energy | 0.32 |
| Speech-motion compatibility | 0.30 |
| Visibility | 0.06 |
2. Select speakers
Replay the released selection from the frozen quality CSV without rerunning window scoring:
python preprocess/build_stageA_select_spk.py \
--input-speaker-csv manifests/speaker_quality.csv \
--input-data-json manifests/train_all_resolved.json \
--output-speaker-csv outputs/selected_speaker_quality.csv \
--output-json outputs/train_selected.json \
--min-pass-rate 0.7 --min-mean-score 0.9 \
--min-mean-speech-motion-score 0.9 --max-outlier-rate 0.01 \
--min-windows 1 --min-passed 1
These thresholds retain speakers with pass rate ≥ 0.70, mean quality ≥ 0.90, mean speech-motion compatibility ≥ 0.90, outlier rate ≤ 0.01, and at least one assessed/passing window.
License and distribution
Authored preprocessing code retains Apache-2.0. Dataset, source components, and weights retain their upstream terms. KaoLRM components include their source license, NVIDIA license, and weight license.