SHOT7M2 Dataset

Overview

• Name: SHOT7M2
• Type: Synthetic, hierarchical, compositional basketball dataset
• Frames: 7.2 million
• Content: Demonstrates hierarchical structure of basketball behaviors
• Generation Method: Based on Starke et al.'s animation model, employing neural state machines (including motion prediction and gating networks) to generate future character poses
• Data Structure: Consists of 4,000 clips, each containing 1,800 frames, with a single agent performing various basketball actions
• Activity Types: 4 (casual play, intense play, dribbling training, no play)
• Action Types: 12
• Skeleton Points: 26 key points
• Combinatorial Behaviors: 14 "Movemes"

Data Format

• File Format: .npy
• Data Splits: Training and test sets in a 32/68 ratio
• Content: 3D pose and behavior annotation data
• Test Set: Includes benchmark_labels.npy for benchmarking
• Reading Method: Use Python scripts to load .npy files