tsinghua-ee/ELLSA2_data

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.