Mercity/Figure-Skating-Classification-Data

AI Skating Coach - Figure Skating Element Recognition Dataset

Clean 64-class version with multi-jump combinations preserved

Overview

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

1. 1.MMFS Dataset (4,915 sequences)
2. 2.2D pose estimation from figure skating competition videos
3. 3.Multiple skaters, various competition levels

1. 1.JSON Motion Capture (253 sequences)
2. 2.Professional 3D mocap capture from 4 elite skaters
3. 3.Converted to 17-keypoint COCO format for consistency

1. 1.Combined & Validated (5,405 sequences)
2. 2.Merged MMFS and mocap data
3. 3.Deduplicated overlapping classes
4. 4.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

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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

1. 1.Pretrain on combinations for sequence context
2. 2.Fine-tune on single jumps for element detection
3. 3.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