Kingston QTM C3D — Ground Truth & v11e Pose Models
Complete dataset for validating markerless pose estimation against Qualisys marker-based motion capture.
Collected at Kingston University London with 9 athletes performing 12 sport science drills.
Downloads
Quick Start
Download everything
1 huggingface-cli download torouni/kingston-qtm-c3d-models kingston-v11e-complete.tar.gz --local-dir .
2 tar -xzf kingston-v11e-complete.tar.gz
Download just the results
1 huggingface-cli download torouni/kingston-qtm-c3d-models kingston-v11e-results.tar.gz --local-dir .
2 tar -xzf kingston-v11e-results.tar.gz
Download ground truth only
1 huggingface-cli download torouni/kingston-qtm-c3d-models kingston-ground-truth.tar.gz --local-dir .
2 tar -xzf kingston-ground-truth.tar.gz
Download individual files (Python)
1 from huggingface_hub import hf_hub_download
2
3 # Single model file
4 hf_hub_download ( "torouni/kingston-qtm-c3d-models" , "student_v11e_52kp_fp16.onnx.data" , local_dir = "." )
5
6 # Single result file
7 hf_hub_download (
8 "torouni/kingston-qtm-c3d-models" ,
9 "results/drills-student-v11e-fp16/cmj/p01-cmj-trial01_results_rtdetr-student-v11e.json" ,
10 local_dir = "."
11 )
Browse and download by category
1 # All inference results (browsable individual files)
2 huggingface-cli download torouni/kingston-qtm-c3d-models --include "results/*" --local-dir .
3
4 # FP16 results only
5 huggingface-cli download torouni/kingston-qtm-c3d-models --include "results/drills-student-v11e-fp16/*" --local-dir .
Repository Contents
torouni/kingston-qtm-c3d-models/
│
│ Archives
├── kingston-v11e-complete.tar.gz # Full package (2.0 GB)
├── kingston-ground-truth.tar.gz # C3D + QTM ground truth (1.0 GB)
├── kingston-v11e-results.tar.gz # Inference results only (223 MB)
│
│ Model weights
├── student_v11e_52kp_fp16.onnx.data # v11e FP16 weights (174 MB)
│
│ Browsable inference results
├── results/
│ ├── drills-student-v11e/ # 191 FP32 JSONs (1.1 GB)
│ └── drills-student-v11e-fp16/ # 191 FP16 JSONs (632 MB)
│
└── README.mdArchive structures
kingston-v11e-complete.tar.gz kingston-v11e-results.tar.gz
├── ground-truth/ ├── v11e-fp32-results/
│ ├── c3d/P01-P09/ (198 files) │ ├── 10-5-5-ball/
│ └── qtm/P01-P09/ (198 files) │ ├── 10m-sprint/
├── results/ │ ├── 5-0-5-left/
│ ├── drills-student-v11e/ │ ├── 5-0-5-right/
│ └── drills-student-v11e-fp16/ │ ├── 5-10-5/
├── models/ │ ├── broad-jump/
│ ├── student_v11e_52kp_fp16.onnx │ ├── cmj/
│ ├── student_v11e_52kp_fp16.onnx.d… │ ├── diamond-dribble-left/
│ ├── student_v11a_52kp_fp16.onnx │ └── diamond-dribble-right/
│ └── student_v11a_52kp.onnx └── v11e-fp16-results/
└── README.md └── (same structure)
kingston-ground-truth.tar.gz
├── c3d/P01-P09/ (198 C3D files)
└── qtm/P01-P09/ (198 QTM files)Dataset Details
Property Value Institution Kingston University London Participants 9 athletes (P01–P09) Drills 12 sport science drills Total sessions 198 Ground truth system Qualisys marker-based motion capture Ground truth formats C3D, QTM Total size (uncompressed) 5.3 GB
Drills
Drill Category Description 10-5-5 Ball Agility Shuttle run with ball (10m-5m-5m) 10m Sprint Speed Maximal 10-meter sprint 5-0-5 Left Agility Change of direction (left) 5-0-5 Right Agility Change of direction (right) 5-10-5 Agility Pro agility shuttle Broad Jump Power Standing broad jump CMJ Power Counter-movement jump Diamond Dribble Left Agility Diamond pattern dribble (left) Diamond Dribble Right Agility Diamond pattern dribble (right)
Model Performance
Evaluated against Qualisys ground truth on the Kingston dataset:
Model PA-MPJPE (mm) vs 3DAT v11e FP16 39.6 32% better v11a FP16 41.2 29% better 3DAT (production) 58.2 baseline
Model Architecture
Base : ViTPose (Vision Transformer for Pose Estimation)Training : Knowledge distillation from SynthPose Teacher (ViTPose-L)Keypoints : 52 (17 COCO + 35 biomechanical markers)Input : 256x192 RGB crop → [1, 3, 256, 192]Output : 52 heatmaps → [1, 52, 128, 96]Format : ONNX (opset 17), FP16 internal computation with FP32 I/O
Inference Result Format
Each JSON contains per-frame, per-athlete pose predictions:
1 {
2 "video_info" : { "input" : "path/to/video.mp4" } ,
3 "model_info" : {
4 "model_name" : "rtdetr-student-v11e-fp16" ,
5 "num_keypoints" : 52 ,
6 "num_coco" : 17 ,
7 "heatmap_size" : [ 128 , 96 ] ,
8 "input_size" : [ 256 , 192 ]
9 } ,
10 "frames" : [
11 {
12 "frame_idx" : 0 ,
13 "athletes" : [
14 {
15 "bbox" : [ x1 , y1 , x2 , y2 ] ,
16 "keypoints" : {
17 "nose" : { "x" : 320.5 , "y" : 180.2 , "confidence" : 0.92 } ,
18 "left_shoulder" : { "x" : 305.1 , "y" : 220.8 , "confidence" : 0.95 }
19 } ,
20 "keypoints_52" : {
21 "sternum" : { "x" : 312.0 , "y" : 260.5 , "confidence" : 0.88 } ,
22 "C7" : { "x" : 311.2 , "y" : 195.3 , "confidence" : 0.85 }
23 }
24 }
25 ]
26 }
27 ]
28 }
52 Keypoint Layout
COCO (1–17): nose, eyes, ears, shoulders, elbows, wrists, hips, knees, ankles
Biomechanical (18–52): sternum, shoulder markers, lateral/medial elbow, lateral/medial wrist, ASIS/PSIS, lateral/medial knee, lateral/medial ankle, 5th metatarsal, toe, big toe, calcaneus, C7, L2, T11, T6
Citation
1 @misc{kingston_qtm_c3d_2026,
2 title={Kingston Sport Science Ground Truth Dataset for Markerless Pose Estimation Validation},
3 author={Nelson AI},
4 year={2026},
5 publisher={Hugging Face},
6 url={https://huggingface.co/torouni/kingston-qtm-c3d-models}
7 }
License
CC BY-NC 4.0 — free for research and non-commercial use.
Links
GitHub (dataset & analysis) : kingston_qtm_c3d_data GitHub (inference pipeline) : vitpose-sports-tracker 
