How AI Body Measurement Works: In-Depth Methodology Explained

AI-driven applications are no longer a revolutionary luxury advancement for businesses; today, it's becoming their need to stay relevant, efficient, and favourable for users. An AI-powered body measurement solution is one of the applications among all these recent innovations that is making way to redefine workflow efficiency, accuracy, and productivity.

AI body measurement solutions are based on a few computer vision, deep learning, and regression models. These AI models provide a strong foundation for this application by providing real-time, contactless, and accurate body measurements.

This might make you inquisitive about how these AI algorithms do such wonders. So through our article, we will explain how each approach for AI body measurements works, what are its advantages and challenges are, so that you can understand which one has the best results for the body measurement task.

AI Body Measurements

AI body measurement solutions are tools driven by AI algorithms and models. These computer vision, deep learning, and regression models empower the solution tool to take body measurements in real time just by taking the user's image as input. This effectively serves as a bridge between personalized customization and accessibility. By allowing users to acquire body measurements remotely, it eventually enables intelligent resource management.

Approaches for AI Body Measurements

The AI body measurement solution utilizes special approaches, including computer vision, deep learning, and regression models. All these approaches have their own way of implementation, benefits, and challenges. To provide you with more clarity on all these AI body measurement approaches, we have provided you with an in-depth explanation of them:

Deep Learning based Computer Vision Model

Deep learning–based computer vision models analyze images or videos to detect landmarks, estimate depth, and reconstruct 3D body meshes. Unlike traditional CV, they leverage neural networks for improved accuracy, scalability, and realistic AI-driven body measurements.

1. OpenPose (CMU)

• How it works: OpenPose methodology uses image feature extraction to find the body joint heatmaps and then utilizes affinity fields to join these identified keypoints into a skeletal structure. This procedure analyzes the spatial patterns in the provided images and locates body landmarks to estimate the pose without needing depth sensors.
• Benefit: This OpenPose methodology enables accurate multi-person pose estimation in real time.
• Challenge: One of the major limitations of this OpenPose methodology is that it produces only 2D data, and for full 3D measurements, it requires additional depth estimation.

2. DensePose (Meta AI)

• How it works:DensePose is a dense human pose estimation in the wild research introduced by Meta AI. This model operates by using pixel-level segmentation to identify every pixel in the found human region and then assigning UV coordinates that map each pixel to a 3D human body model. This creates a dense relation between the 2D image and 3D body geometry, extending high-precision measurement extraction. This can then be used as a foundation for extracting precise body measurements.
• Benefit: This DensePose model provides direct dense 3D mapping for precise body measurements.
• Challenge: But this DensePose model is computationally heavy and less suitable for low-power or real-time applications.

3. MediaPipe Pose (Google)

• How it works:The MediaPipe Pose is a research introduced by Google that consists of a two-step CV pipeline. In the first step, it utilizes object identification to find the person within the frame. After this, the landmark regression is applied to predict 33 3D keypoints (x, y, z). This method uses visual pattern identification to estimate body positions efficiently without requiring advanced hardware.
• Benefit: The Mediapipe Pose model for body measurement is very lightweight and capable of running in real time on mobile devices.
• Challenge: Although this Medipipe pose is very lightweight but it has limited accuracy for fine measurements, which makes it a suitable choice for activity tracking rather than detailed garment fitting. MediaPipe predicts relative depth, not absolute scale so accurate garment fitting requires external calibration, such as user height. It is more suitable for activity tracking than precise tailoring.

3D Reconstruction Deep Learning Models

Deep learning models driven methodology for body measurements utilizes a neural network to analyze the input images, find the landmarks, recreate the 3D human meshes, and provide an accurate estimation for the dimensions of the body. Below, we have mentioned some famous deep learning models that have made a significant impact on AI-driven body measurements:

1. SMPL + HMR (Kanazawa et al., 2018)

• How it works: “SMPL (Skinned Multi-Person Linear) parametric human body models work in combination with the HMR (Human Mesh Recovery) model, which is a deep learning framework. HMR has a convolutional neural network to process a single RGB image and directly predict the SMPL parameters, which include pose, shape, and camera, to reconstruct a full 3D human mesh with 6890 vertices and 2D/3D keypoints”. (Kanazawa et al., 2018)
• Benefit: This combined approach of SMPL and HMR enables end-to-end prediction of a realistic 3D human body mesh just through a single image.
• Challenge: SMPL, combined with the HMR approach for body measurement, has high chances of alignment errors without additional camera-aware processing.

2. CLIFF (Li et al., 2022)

• How it works: “CLIFF's approach for body measurements is built on HMR but comes with camera-aware features. This function models the relationship between the camera and the subject. Using perspective constraints improves scale, depth, and alignment accuracy. It assists the predicted SMPL mesh to match real-world proportions, which is essential for measurement applications.” (Zhihao et al., 2022)
• Benefit: This CLIFF model has a major role in contributing to more accurate scaling and depth estimation than HMR, making it an ideal AI body measurement.
• Challenge: With these advantages, the major limitation of CLIFF is that it is computationally more demanding than HMR due to the added camera modelling steps

3. Hourglass Networks

• How it works: Hourglass networks are another deep learning model-driven body measurements approach. It is designed for extending high-precision pose estimation. Under this approach, the provided image features are repeatedly downsampled and upsampled, forming an hourglass shape. This allows the network to understand both local details and global spatial context, which effectively refines landmarks across multiple stages to provide accurate measurement extraction.
• Benefit: Hourglass networks ensure the provision of accurate keypoints, even in complex poses.
• Challenge: This hourglass network model for body measurements demands large annotated datasets and high computational resources for training, which makes it a bit expensive.

Regression Models

The regression model for body measurements works by scanning the features within the input image, and learning a mathematical relationship, to ensure fast and complexity body accurate body measurements. So, these models are simple but have limited accuracy compared to deep learning models. Here, we have mentioned some regression models that can play a significant role in providing accurate body measurements:

1. Linear Regression with Pixel-to-Cm Calibration

• How it works: Linear regression with pixel-to-cm calibration works by extracting measurable features from the input image, like pixel distances between the landmarks. Then it converts them into real-world values using a reference object or the person's height. After this, these pixel values are then mapped to actual body measurements.
• Benefit: The regression model exhibits fast and computationally inexpensive performance for doing the body measurement task.
• Challenge: Although regression models are fast and inexpensive but they demonstrate limited accuracy if the calibration reference is missing or incorrectly estimated.

2. Random Forest Regression

• How it works: The Random Forest regression methodology for providing AI-assisted body measurements is built on decision trees that utilize the extracted features like ratios and pixel distances. It combines predictions made from multiple trees and records complex nonlinear relationships between features and body dimensions.
• Benefit: This random forest regression approach for body measurements can handle non-linear relationships well and reduce overfitting.
• Challenge: To provide the expected performance, it demands a sufficiently large and diverse labeled dataset, which stands as a major limitation for getting good accuracy.

3. Support Vector Regression (SVR)

• How it works: Support Vector Regression methodology for body measurement implements the principle of support vector machines for regression tasks. It works by mapping the extracted body features into a higher-dimensional space and has a function within a defined margin of error to provide precise measurements, even by being trained on limited data.
• Benefit: The SVR-powered approach for body measurements has shown promising accuracy even while being on relatively smaller datasets.
• Challenge: SVR-driven methodology can get computationally expensive for large datasets and might be sensitive to parameter tuning.

CV vs Deep Learning Models Vs Regression Models: Which one is best?

All these models used for AI body measurement have their unique ways of implementation, abilities, and challenges. So to decide on the right approach, it's important to understand which approach aligns well with your specific business requirements, both in terms of budget and performance. To solve this confusion, we have provided you with a comparison between all these AI body measurement approaches based on key parameters that you should consider:

So the choice of the right approach for AI body measurements relies on your requirements. If you are looking for fast, lightweight, real-time landmark detection where depth accuracy isn’t critical, then you should consider a deep learning based CV approach. But if you need highly accurate, scalable, and realistic 3D measurements, then deep learning approaches are the best fit, although they need more cost, data, and compute. whereas regression model-driven approaches stand as a suitable choice for simple and low-cost deployments in which datasets and hardware are limited.

Decision for Optimal Body Measurement

Body measurement technologies span from deep learning based Computer Vision techniques to advanced Deep Learning models and lightweight Regression approaches, each with distinct strengths.

The deep learning based Computer Vision offers a good trade-off between accuracy and efficiency. Deep Learning delivers state-of-the-art precision and adaptability, but at higher computational and data costs, and Regression models provide fast, resource-light solutions for simpler needs.

The choice depends on the intended application; industries demanding millimeter-level accuracy, like high end fashion or healthcare, benefit most from Deep Learning, while mobile fitness apps or quick-fit estimators can thrive on Computer Vision or Regression. Ultimately, aligning technological capabilities with operational resources and accuracy requirements ensures optimal performance and scalability.

Struggling to choose the right AI body measurement technique without overspending or compromising accuracy? Discuss with our experts at Centrox AI, and find the solution to your problem.