SpinePose is a family of 2D human pose estimation models trained to estimate a 37-keypoint skeleton, extending standard human body models to include the spine, pelvis, and feet regions in detail.
Four SpinePose variants (small, medium, large, and x-large) are available, with 0.72, 1.98, 4.22, and 17.37 GFLOPS respectively at inference time. V1 models were trained on the SpineTrack dataset and V2 models were fine-tuned on the SIMSPINE dataset, published in our CVSports @ CVPR 2025 and CVPR 2026 papers, respectively.
This automatically downloads the correct ONNX checkpoint.
Run spinepose -h for detailed usage options.
Python API
python
1import cv2
2from spinepose import SpinePoseEstimator
34# Initialize estimator (downloads ONNX model if not found locally)5estimator = SpinePoseEstimator(device='cuda')67# Perform inference on a single image8image = cv2.imread('path/to/image.jpg')9keypoints, scores = estimator.predict(image)10visualized = estimator.visualize(image, keypoints, scores)11cv2.imwrite('output.jpg', visualized)
For higher-level use:
python
1from spinepose.inference import infer_image, infer_video
23# Single image inference4infer_image('path/to/image.jpg','output.jpg')56# Video inference with optional temporal smoothing7infer_video('path/to/video.mp4','output_video.mp4', use_smoothing=True)
Evaluation
To reproduce results, prepare the following directory layout:
Each PyTorch checkpoint contains both teacher and student weights, with only the student used during inference. Exported ONNX checkpoints only contain the student.
Metrics
We report Average Precision (AP) and Average Recall (AR) under varying Object Keypoint Similarity (OKS) thresholds, consistent with COCO conventions but extended to the 37-keypoint SpineTrack format.
Results
Method
Train Data
Kpts
COCO
Halpe26
Body
Feet
Spine
Overall
Params (M)
FLOPs (G)
AP
AR
AP
AR
AP
AR
AP
AR
AP
AR
AP
AR
SimCC-MBV2
COCO
17
62.0
67.8
33.2
43.9
72.1
75.6
0.0
0.0
0.0
0.0
0.1
0.1
2.29
0.31
RTMPose-t
Body8
26
65.9
71.3
68.0
73.2
76.9
80.0
74.1
79.7
0.0
0.0
15.8
17.9
3.51
0.37
RTMPose-s
Body8
26
69.7
74.7
72.0
76.7
80.9
83.6
78.9
83.5
0.0
0.0
17.2
19.4
5.70
0.70
SpinePose-s
SpineTrack
37
68.2
73.1
70.6
75.2
79.1
82.1
77.5
82.9
89.6
90.7
84.2
86.2
5.98
0.72
SimCC-ViPNAS
COCO
17
69.5
75.5
36.9
49.7
79.6
83.0
0.0
0.0
0.0
0.0
0.2
0.2
8.65
0.80
RTMPose-m
Body8
26
75.1
80.0
76.7
81.3
85.5
87.9
84.1
88.2
0.0
0.0
19.4
21.4
13.93
1.95
SpinePose-m
SpineTrack
37
73.0
77.5
75.0
79.2
84.0
86.4
83.5
87.4
91.4
92.5
88.0
89.5
14.34
1.98
RTMPose-l
Body8
26
76.9
81.5
78.4
82.9
86.8
89.2
86.9
90.0
0.0
0.0
20.0
22.0
28.11
4.19
RTMW-m
Cocktail14
133
73.8
78.7
63.8
68.5
84.3
86.7
83.0
87.2
0.0
0.0
6.2
7.6
32.26
4.31
SimCC-ResNet50
COCO
17
72.1
78.2
38.7
51.6
81.8
85.2
0.0
0.0
0.0
0.0
0.2
0.2
36.75
5.50
SpinePose-l
SpineTrack
37
75.2
79.5
77.0
81.1
85.4
87.7
85.5
89.2
91.0
92.2
88.4
90.0
28.66
4.22
SimCC-ResNet50*
COCO
17
73.4
79.0
39.8
52.4
83.2
86.2
0.0
0.0
0.0
0.0
0.3
0.3
43.29
12.42
RTMPose-x*
Body8
26
78.8
83.4
80.0
84.4
88.6
90.6
88.4
91.4
0.0
0.0
21.0
22.9
50.00
17.29
RTMW-l*
Cocktail14
133
75.6
80.4
65.4
70.1
86.0
88.3
85.6
89.2
0.0
0.0
8.1
8.1
57.20
7.91
RTMW-l*
Cocktail14
133
77.2
82.3
66.6
71.8
87.3
89.9
88.3
91.3
0.0
0.0
8.6
8.6
57.35
17.69
SpinePose-x*
SpineTrack
37
75.9
80.1
77.6
81.8
86.3
88.5
86.3
89.7
89.3
91.0
88.9
89.9
50.69
17.37
SpineTrack Dataset
The SpineTrack dataset comprises both real and synthetic data:
SpineTrack-Real: Annotated natural images with nine detailed spinal landmarks in addition to COCO joints.
SpineTrack-Unreal: Synthetic subset rendered in Unreal Engine with biomechanically aligned OpenSim annotations.
In both cases, the dataset will download two zipped folders: annotations (24.8 MB) and images (19.4 GB), which can be unzipped to obtain the following structure:
All annotations follow the COCO format, directly compatible with MMPose, Detectron2, or similar frameworks.
The synthetic subset was primarily employed within the active learning pipeline used to bootstrap and refine annotations for real-world images.
All released SpinePose models were trained exclusively on the real portion of the dataset.
[!WARNING]
A small number of annotations in the synthetic subset are corrupted.
We recommend avoiding their use until the updated labels are released in the next dataset version.
Citation
If you use SpinePose or SpineTrack in your research, please cite:
BibTeX:
bibtex
1@InProceedings{Khan_2025_CVPR,
2 author = {Khan, Muhammad Saif Ullah and Krau{\ss}, Stephan and Stricker, Didier},
3 title = {Towards Unconstrained 2D Pose Estimation of the Human Spine},
4 booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
5 month = {June},
6 year = {2025},
7 pages = {6171-6180}
8}
APA:
Khan, M. S. U., Krauß, S., & Stricker, D. (2025). Towards Unconstrained 2D Pose Estimation of the Human Spine. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 6172-6181).
