Human Pose Estimation (HPE) is the task of estimating a person’s pose from an image or video by detecting keypoints—such as joints (elbow, shoulder, hip, knee) or other anatomical landmarks. This problem is commonly formulated as a regression task, where a model predicts the spatial coordinates of these keypoints given an input image.
Pose Estimation Techniques
Pose estimation approaches can be categorized based on five key dimensions:
Different input types influence the model’s ability to infer pose under various conditions:
- RGB Standard images or video frames from a single camera.
- RGB-D Combines RGB images with depth data (e.g., from Kinect or stereo cameras) to provide 3D spatial information.
- Video Utilizes temporal information across frames to enhance pose consistency and smoothness.
- Multi-View Uses images from multiple cameras to reconstruct a more accurate pose.
Pose estimation models follow different strategies for keypoint detection:
- Top-down First detects human instances (bounding boxes) and then estimates keypoints for each person.
- Bottom-up Detects all keypoints in an image first and then groups them into individual persons.
- Single-stage Predicts keypoints directly without explicit detection or grouping steps.
Different models define how human pose is structured:
- Skeleton-based Represents poses as a set of keypoints connected by edges (common in 2D and 3D estimation)
- Planar models Use additional shape constraints, such as contours or silhouettes, to refine keypoint locations.
- Parametric (skinned) models Statistical shape models used for human mesh recovery (e.g., SMPL, GHUM, FLAME), which provide a full 3D body representation beyond just keypoints.
- Biomechanical models Physics-based models incorporating muscles, forces, and torques, such as AnyBody, OpenSim, and SKEL.
Pose estimation methods predict keypoints in either:
- 2D Estimates (x, y) coordinates in pixel space.
- 3D Estimates (x, y, z) coordinates in metric space, often requiring depth estimation or multi-view images.
- Heatmap-based Regression Predicts heatmaps where the peak intensity represents keypoint locations.
- Direct Coordinate Regression Uses a neural network to directly predict (x, y) or (x, y, z) coordinates.
- Coordinate Classification Discretizes the output space into bins and classifies keypoint locations.
Hover over the terms to view detailed explanations.
This classification is not exhaustive, and many works combine elements from different categories. For example, a model may use RGB-D images for 3D pose estimation, a top-down approach for multi-person detection, and a heatmap-based regression for keypoint prediction.
Pose Estimation Pipelines
Pose estimation models typically follow a structured pipeline that transforms raw input data into accurate keypoint predictions. This pipeline consists of three key stages:
1. Preprocessing
Before feeding an image or video into a pose estimation model, various preprocessing techniques are applied to improve robustness and generalization:
- Normalization: Rescales pixel values to a standard range (e.g., \([0,1]\) or \([-1,1]\)) to ensure numerical stability during training.
- Data Augmentation: Applies transformations such as rotation, scaling, flipping, and color jittering to enhance model generalization.
- Cropping & Resizing: Ensures consistent input dimensions, especially in top-down approaches where bounding boxes are extracted.
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Keypoint Encoding: For heatmap-based methods, ground-truth keypoints \((x_i, y_i)\) are converted into Gaussian heatmaps:
\[H_i(x, y) = \exp \left(-\frac{(x - x_i)^2 + (y - y_i)^2}{2\sigma^2} \right)\]where \(\sigma\) controls the spread of the heatmap.
2. Inference
Once preprocessed, the image is passed through a neural network, which predicts keypoint locations using one of the following strategies:
- Heatmap-based Regression: The model outputs a probability distribution over possible keypoint locations, where the highest-intensity region corresponds to the estimated keypoint.
- Direct Coordinate Regression: The model directly predicts keypoint coordinates \((x, y)\) in 2D or \((x, y, z)\) in 3D.
- Transformer-Based Methods: Recent approaches leverage attention mechanisms to capture long-range dependencies and improve pose estimation accuracy.
3. Post-Processing
After obtaining raw predictions from the model, post-processing techniques refine the results:
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Argmax or Softmax: For heatmap-based methods, the keypoint position is extracted as:
\[(x_i, y_i) = \arg \max_{(x, y)} H_i(x, y)\]where \(H_i(x, y)\) is the heatmap for keypoint \(i\).
- Pose Refinement: Techniques such as temporal smoothing (for video), spatial optimization (e.g., Perspective-n-Point (PnP) for 3D), and model-based constraints (e.g., kinematic priors) improve accuracy.
- Multi-Person Association: Bottom-up approaches require keypoint grouping to distinguish between individuals.
Each stage plays a crucial role in ensuring accurate and robust human pose estimation, especially when dealing with challenging scenarios such as occlusions, motion blur, or extreme poses.
