Datasets:

Mercity
/

Figure-Skating-Classification-Data

Figure skating skeleton pose sequences for action/element classification. Raw keypoint data extracted from competition videos and professional motion capture, presented in clean unmodified form.

Total samples: 5,405
Training: 4,324 sequences
Test: 1,081 sequences
Classes: 64 figure skating elements
Format: Clean unaugmented data (no synthetic samples, no class weights)

Dataset Structure

├── train_data.pkl          # Training sequences (4,324)
├── train_label.pkl         # Training labels
├── test_data.pkl           # Test sequences (1,081)
├── test_label.pkl          # Test labels
├── label_mapping.json      # Class IDs and names
└── dataset_info.json       # Metadata

Data Format

Skeleton sequences: (num_samples, variable_frames, 17_keypoints, 3_coordinates)

Frames: Variable length from original footage (original temporal resolution preserved)
Duration: Varies by element (typically 2-25 seconds at 30 fps)
Keypoints: 17-point COCO format
- Head: nose, left/right eye, left/right ear
- Torso: shoulders, elbows, wrists, hips, knees, ankles
Coordinates: (x, y, confidence) normalized to [-1, 1] range

Classes (64 Total)

Single Jump Elements (0-20)

Single rotation jumps: Axel, Flip, Lutz, Loop, Salchow, Toeloop
Rotations: 1x, 2x, 3x, 4x (where applicable)

Examples: 1Axel, 2Flip, 3Lutz, 4Toeloop

Multi-Jump Combinations (21-30)

Natural sequence patterns from competition:

1A+3T, 1A+3A
2A+3T, 2A+3A, 2A+1Eu+3S
3F+3T, 3F+2T+2Lo
3Lz+3T, 3Lz+3Lo
Generic Combination (Comb)

Spins (31-62)

Rotational elements with position changes:

FCSp (Foot Change Camel Spin): 31-34
CCoSp (Catch Foot Combination Spin): 35-38
ChCamelSp (Change Camel Spin): 39-42
ChComboSp (Change Combination Spin): 43-46
ChSitSp (Change Sit Spin): 47-50
FlySitSp (Fly Sit Spin): 51-54
LaybackSp (Layback Spin): 55-58

Step Sequences & Choreography (59-63)

Linear traveling skating patterns:

StepSeq1-4: Graded step sequences (59-62)
ChoreSeq1: Choreographed sequence (63)

Data Sources

MMFS Dataset (4,915 sequences)
- 2D pose estimation from figure skating competition videos
- Multiple skaters, various competition levels
JSON Motion Capture (253 sequences)
- Professional 3D mocap capture from 4 elite skaters
- Converted to 17-keypoint COCO format for consistency
Combined & Validated (5,405 sequences)
- Merged MMFS and mocap data
- Deduplicated overlapping classes
- Combinations preserved for sequence modeling

Preprocessing

Format unification: 142-marker mocap → 17-keypoint COCO skeleton
Temporal sampling: Uniform to 150 frames per sequence
Normalization: Keypoint coordinates normalized to [-1, 1]
Velocity features: Computed for temporal dynamics
Train/test split: 80/20 stratified by class

Loading the Dataset

Python

import pickle
import json
import numpy as np

# Load training sequences and labels
with open('train_data.pkl', 'rb') as f:
    X_train = pickle.load(f)  # List of (150, 17, 3) arrays
with open('train_label.pkl', 'rb') as f:
    y_train = pickle.load(f)  # Array of class IDs (0-63)

# Load test data
with open('test_data.pkl', 'rb') as f:
    X_test = pickle.load(f)
with open('test_label.pkl', 'rb') as f:
    y_test = pickle.load(f)

# Load class mapping
with open('label_mapping.json', 'r') as f:
    mapping = json.load(f)

# Inspect
print(f"Training: {len(X_train)} sequences, {X_train[0].shape}")
print(f"Classes: {len(np.unique(y_train))}")
print(f"Class weights: {np.bincount(y_train)}")  # Raw distribution

Convert to NumPy

import numpy as np

# Stack sequences into array
X_train_array = np.array(X_train)  # (4324, 150, 17, 3)
X_test_array = np.array(X_test)    # (1081, 150, 17, 3)

Recommended Usage

Action Recognition

CNN-LSTM architecture for 64-class classification
Input: (batch, 150, 17, 3) sequences
Output: 64-class softmax

Sequence Modeling

Use combinations (classes 21-30) for multi-step skill prediction
Temporal modeling with RNNs/Transformers
Learn natural skill progression patterns

Transfer Learning

Pretrain on combinations for sequence context
Fine-tune on single jumps for element detection
Apply to event/routine-level classification

Sports Analytics

Skill difficulty assessment
Athlete performance tracking
Technique consistency analysis

Class Distribution

For detailed per-class sample counts, see dataset_info.json

Imbalance ratio: ~6x (largest/smallest class)
Skew: Toward more common elements (2-3 rotations, standard spins)

Dataset compiled from public figure skating competition videos and proprietary motion capture data. Use for research and educational purposes.

Generated: February 2026