MediaPipe Pose Skeleton Images

• MediaPipe Pose skeleton images are structured visual encodings of human pose derived from 2D/3D keypoint detection, providing clear geometric representations.
• They transform complex video content into simplified skeleton graphs, facilitating robust activity recognition and pose classification.
• Their advanced design, including tree-structure encodings and deep learning pipelines, enhances performance in augmented reality, healthcare, and sports analytics.

MediaPipe Pose skeleton images are structured visual encodings of articulated human pose, generated by the MediaPipe framework based on 2D (and optionally 3D) keypoint detection. These skeleton images abstract raw video or image content into geometric representations by rendering detected body joints and their anatomical connections, thereby facilitating downstream tasks such as activity recognition, pose classification, and spatio-temporal analysis. MediaPipe employs deep learning-based landmark extraction pipelines optimized for real-time performance, robustness to occlusion, and deployment on edge devices. Skeleton images offer significant advantages in interpretability and generalization for vision models by distilling pose geometry and reducing the influence of scene context or appearance variations.

1. Keypoint Extraction and Normalization

MediaPipe Pose utilizes a lightweight convolutional backbone (Mobilenet-based) within its BlazePose detector to isolate human subjects in a frame, regress up to 33 body landmarks, and predict normalized $(x_i, y_i, z_i)$ joint coordinates with associated confidence scores $c_i$ (Sengar et al., 21 Jun 2024, Radhakrishna et al., 2023, Mohiuddin et al., 29 Nov 2025). In practical pipelines, initial frames are captured and pre-processed—often normalized or resized to a fixed input dimension (e.g., $256\times 256$ or $224\times 224$), and pixel values are standardized.

Landmark regression produces normalized outputs in $[0,1] \times [0,1]$ for $(x,y)$, with $z$ serving as a learned relative depth. Pixel coordinates are recovered via:

$$\text{pixel}_x^i = x^i \cdot W,\quad \text{pixel}_y^i = y^i \cdot H$$

where $W$, $H$ are frame width and height (Sengar et al., 21 Jun 2024, Radhakrishna et al., 2023). For multi-frame video, temporal smoothing (exponential moving average, Kalman filtering) is applied to stabilize joint positions, notably in dynamic or occluded scenarios.

2. Skeleton Graph Structures and Rendering

The skeleton is formally modeled as an undirected graph $G=(V,E)$, where vertices $V$ enumerate MediaPipe landmarks ($0, \ldots, 32$ for basic body pose; up to $N=68$ with hands and face included) and edges $E$ encode anatomical adjacency (e.g., $(11,\!13)$ for left shoulder–elbow, $(12,\!14)$ for right shoulder–elbow) (Sengar et al., 21 Jun 2024, Radhakrishna et al., 2023, Laines et al., 2023). Adjacency matrices $A_{ij} = 1$ if $(i,j) \in E$ support efficient rendering and processing.

For image generation, a blank canvas of fixed dimensions (e.g., $224\times224$) is populated by plotting landmarks as colored circles and bones as lines linking adjacent joints. Color coding and thickness are optionally modulated by confidence scores and depth values:

• Thickness $$d_{ij} = \text{clip}(k/\bar{Z}_{ij}, d_\text{min}, d_\text{max})$$ where $\bar{Z}_{ij}$ is average normalized depth.
• Hue $H_{ij} = 120^\circ \cdot \min(c_i, c_j)$ mapped via HSV color space (Sengar et al., 21 Jun 2024).

Skeleton images can be rendered with blending over the input image, typically using an alpha value ($\alpha_\text{line}$), and optimized for real-time GPU acceleration through preallocated buffers and unified shader pipelines.

3. Advanced Skeleton Representations: Tree-Structure Skeleton Images (TSSI)

For spatio-temporal tasks, especially sign language recognition, sequence-based skeleton images are constructed as Tree-Structure Skeleton Images (TSSI) (Laines et al., 2023). In TSSI:

• Columns represent skeleton joints in a depth-first tree traversal order (DFS) over the pose graph.
• Rows encode temporal evolution (frames).
• Channels store raw or normalized $(x,y,z)$ joint coordinates for each landmark.

Formally, for a sequence of $T$ frames and $N$ landmarks (with $M = 2N-1$ DFS visits), the TSSI tensor is $S \in \mathbb{R}^{T \times M \times 3}$. Fixed image height $H$ is achieved via temporal rescaling. Data augmentation (e.g., scaling, flipping, speed warp) is applied at the joint level before TSSI assembly, enhancing model generalization.

Common caveats for TSSI construction with MediaPipe data include missing detections (replace hand/face joints with wrist/nose), confidence thresholding (ignore $c<0.3$), and relative unreliability of $z$ for metric depth (Laines et al., 2023).

4. Skeleton Images as Model Inputs and Classification Efficacy

Skeleton images derived from MediaPipe Pose provide strong inductive biases for deep learning models, substantially surpassing raw image inputs in tasks such as yoga pose classification—demonstrated with the Yoga-16 dataset (Mohiuddin et al., 29 Nov 2025). After extraction:

• Images are resized (e.g., $224 \times 224 \times 3$), normalized to $[0,1]$, and augmented (random rotations, translations, flips).
• Models such as VGG16, ResNet50, Xception are adapted by retraining the classifier head with dense layers (global average pooling, ReLU, Softmax), and freezing or fine-tuning backbone layers depending on initialization.

Empirical evaluation yields the following performance (accuracy, precision, recall, F1), with MediaPipe skeletons outperforming direct images and YOLOv8 skeletons:

Model	Raw Image Acc.	MediaPipe Skel. Acc.	YOLOv8 Skel. Acc.
VGG16	86.33%	96.09%	91.41%
ResNet50	66.41%	88.28%	75.00%
Xception	84.38%	93.36%	85.55%

MediaPipe skeleton images excel due to landmark anatomical consistency, background removal, and richer joint connectivity (33 vs. 17 joints).

5. Quaternion Extraction and Orientation Estimation

MediaPipe Pose skeletons can support 3D orientation estimation through quaternion extraction using approximate 3D joint locations (Radhakrishna et al., 2023). Typical algorithms:

• Use left/right shoulder points for the local $z$-axis.
• Employ camera world-up as canonical $z$; compute orthonormal basis via cross products.
• Assemble rotation matrix $R$ and convert to quaternion $Q={a, b, c, d}$ using trace-based formulas.
• Extract heading angle $\theta$ about the vertical, optionally filtered for smoothness (e.g., Kalman filter).

Reported end-to-end latency is sub-50 ms on edge devices (ARM, Intel i5), including skeleton rendering and quaternion computation. Limitations include unreliable absolute depth, occlusion-induced noise, and 2D-to-3D misalignment; mitigations involve temporal filtering and sensor fusion.

6. Applications, Limitations, and Future Directions

MediaPipe Pose skeleton images are integral to systems for augmented reality, healthcare monitoring, sports analytics, and sign language/video gesture recognition (Sengar et al., 21 Jun 2024, Mohiuddin et al., 29 Nov 2025, Laines et al., 2023). Their abstracted, pose-centric representations are especially suited to environments requiring efficiency and robustness to visual context.

Limitations include:

• Loss of out-of-plane information in 2D skeletons.
• Propagation of landmark detection errors.
• Omission of silhouette/context cues that may be relevant for fine-grained classification.

Potential directions for enhanced efficacy are:

• 3D pose estimation and stereo input fusion.
• Multi-stream deep networks combining RGB and skeleton channels.
• Graph-convolutional models leveraging the skeleton graph structure.
• Expansion of datasets to encompass varied poses, temporal transitions, and increased diversity (Mohiuddin et al., 29 Nov 2025).

Tree-structure encodings like TSSI broaden the applicability for temporal activity recognition, with augmentation strategies and confidence handling critical under real-world variability (Laines et al., 2023). The data suggest continued evolution toward integrated, multimodal, spatio-temporal representations leveraging efficient pose skeleton pipelines.