3D Pose Estimation Using Teacher-Student Model Compression
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Solution Overview
Problem
Current methods for estimating the 3D pose of objects in images require expensive sensor equipment and depth information, limiting natural movement and accuracy due to the need for inertial measurement devices or markers, and are not feasible for real-time pose estimation using general RGB cameras.
Innovation Solution
A method is developed to estimate the 3D pose of objects using a pretrained prediction model that learns from 2D input images, generating animation data to represent object movement without requiring depth information, by training a teacher model and a further compressed student model to build a prediction model capable of real-time pose estimation on devices like smartphones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor equipment such as inertial measurement devices or markers are attached to an object to acquire sensing data, then depth information and movement detection accuracy are improved, but cost increases and natural movement is limited
Solution Approach 1:
The patent extracts the depth information requirement from the system by using a monocular RGB camera instead of depth cameras or sensor equipment. The 3D pose estimation is achieved by processing only 2D image data through neural networks, eliminating the need for attached sensors while still obtaining depth-related information
Solution Approach 2:
The patent creates a virtual 3D representation (copy) of the object's pose from 2D image data. Instead of measuring physical depth directly with sensors, the system generates a copied 3D pose model that mimics the actual object position and orientation through image processing and neural network prediction
2Ease of operation
If a prediction model is built for real-time 3D pose estimation using only RGB cameras, then ease of operation and accessibility are improved, but model size and computational requirements increase
Solution Approach 1:
The patent segments the pose estimation task into two parts: a teacher model that processes images to generate intermediate results, and a student model that is further compressed and optimized for real-time deployment. This segmentation allows the system to maintain high accuracy while reducing the computational burden on mobile devices
Solution Approach 2:
The patent changes the parameters of the prediction model through compression techniques. The student model uses reduced precision arithmetic (e.g., quantization from 32-bit to 8-bit integers) and optimized network architecture parameters, enabling real-time execution on mobile devices while maintaining acceptable accuracy
3Productivity
If a compressed prediction model is built for mobile devices, then productivity and real-time processing are improved, but estimation accuracy may deteriorate
Solution Approach 1:
The patent performs preliminary processing through the teacher model before the student model makes final predictions. The teacher model generates intermediate results and guidance that help the compressed student model achieve accurate predictions faster, effectively preparing the data in advance to compensate for the student model's reduced precision
Solution Approach 2:
The patent implements a feedback mechanism where the teacher model's outputs are used to guide and correct the student model's predictions. This feedback loop allows the compressed model to learn from the more accurate teacher model, maintaining estimation precision while achieving real-time processing speeds
Data Source
AI summary
Provided is a method of generating data for estimating a three-dimensional (3D) pose of an object included in an input image, the method including acquiring the input image including at least one moving object, estimating location information about each joint of a plurality of joints of the object included in the input image using a prediction model pretrained to estimate the location information about each joint of the plurality of joints of the object, and generating animation data that represents a movement of the object based on the estimated location information.