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DIEM-A Challenge Dataset
Dataset for the MMAC@ACII 2026 Challenge: Multilingual and Multimodal Affective Computing Workshop. The task is to classify performer emotional intent from full-body motion capture data across 12 emotion categories: anger, contempt, disgust, fear, joy, sadness, surprise, gratitude, guilt, jealousy, shame, and pride.
Dataset Overview
- Source: DIEM-A (Diverse Intercultural E-Motion Database of Asian Performers) dataset
- Performers: 92 professional artists (49 Japanese, 43 Taiwanese)
- Modalities: 3 motion capture formats (
.bvh,.fbx,.c3d) - Skeleton: 24 joints
- Intensity levels: Low (L), Medium (M), High (H)
| Split | Performers | Sequences | Labels |
|---|---|---|---|
| Train | 74 (40 JP, 34 TW) | 7,992 | Provided (emotion + scenario text) |
| Test | 18 (9 JP, 9 TW) | 1,944 | Hidden |
Structure
.
├── bvh/
│ ├── train/ # 7,992 BVH files
│ └── test/ # 1,944 BVH files
├── fbx/
│ ├── train/
│ └── test/
├── c3d/
│ ├── train/
│ └── test/
├── train_data.csv # Labels, scenarios, and metadata
├── test_data.csv # Metadata only (no labels or scenarios)
└── README.md
Baseline
STGCN++ with leave-performer-out 10-fold cross-validation:
| Metric | Score |
|---|---|
| Accuracy | 27.1% (\u00b13.7%) |
| Macro-F1 | 25.2% (\u00b14.5%) |
Random baseline: 8.33%. See the benchmark repo for code and details.
Evaluation
Submissions are ranked by Macro-F1 and Accuracy on the hidden test set. A bonus explainability award is also given.
Challenge
- Website: MMAC@ACII 2026
- Benchmark: diema-challenge-benchmark
Citation
If you use this dataset, please cite the DIEM-A dataset paper (see challenge website for details).
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