Transformer Network for 3D Human Pose Estimation
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Solution Overview
Problem
Current 3D human pose estimation from single images faces challenges due to the need for manual encoding of joint relationships and over-smoothing issues in graph convolutional networks, limiting their ability to accurately estimate 3D poses from diverse datasets without leveraging spatial relationships effectively.
Innovation Solution
A transformer-based neural network architecture that includes a joint embedding mechanism, a joint transformer for predicting intermediate 3D joints and errors, and a refinement transformer for estimating final 3D joints, allowing the network to learn correlations between joints dynamically without manual encoding, and using error prediction and intermediate supervision to refine predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If graph convolutional networks are used for 3D pose estimation, then spatial relationships between joints can be modeled, but manual encoding of joint relationships is required which increases device complexity
Solution Approach 1:
The transformer-based network automatically learns and encodes the kinematic relationships between joints through self-attention mechanisms, eliminating the need for manual encoding of adjacency matrices. The network serves itself by discovering joint relationships directly from the data during training, rather than requiring pre-defined graph structures.
Solution Approach 2:
The patent replaces the mechanical graph convolutional approach with a transformer-based attention mechanism. Instead of using fixed graph structures with manual adjacency matrices, the system uses dynamic self-attention to model joint relationships, substituting the rigid mechanical graph structure with a more flexible attention-based system.
2Measurement precision
If multiple graph convolutional network layers are stacked to improve pose estimation, then spatial relationships are better captured, but over-smoothing occurs which degrades performance
Solution Approach 1:
The patent substitutes the graph convolutional network architecture with a transformer-based architecture that uses self-attention mechanisms. This replacement avoids the over-smoothing problem inherent in stacked GCN layers while maintaining the ability to capture spatial relationships between joints through attention weights that dynamically model joint dependencies.
Solution Approach 2:
The transformer-based system introduces dynamic attention mechanisms that adaptively weight the importance of different joint relationships based on the input pose, rather than applying fixed convolutional operations. This dynamic approach allows the model to capture spatial relationships without the degradation caused by repeated convolutional layers.
3Device complexity
If standard graph convolutions with shared weight matrices are used, then model simplicity is maintained, but the ability to model different relationships between body joints is limited
Solution Approach 1:
The transformer-based network applies local quality by allowing different attention mechanisms to operate on different joint pairs, enabling the model to capture unique relationships between specific body joints. Each joint can have its own attention pattern, allowing the system to model diverse relationships across different body parts rather than applying a uniform shared weight matrix.
Solution Approach 2:
The system transitions from static shared weight matrices to dynamic attention mechanisms that adaptively adjust the importance of different joint relationships based on the input context. This allows the model to flexibly model different body joint relationships without being constrained by a fixed weight structure.
Data Source
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AI summary
Disclosed is a transformer based neural network architecture for estimating a 3D pose from a 2D pose, that includes a joint embedding mechanism for receiving a plurality of 2D joint positions, setting each 2D joint position as a token, and increasing a dimension of each token to a predefined higher dimension. The transformer based neural network architecture further includes a joint transformer communicatively coupled to the joint embedding mechanism, for predicting a plurality of intermediate 3D joints, and a corresponding plurality of prediction errors, from corresponding plurality of tokens of the increased dimension, and a refinement transformer for estimating a plurality of final 3D joints based on the plurality of intermediate 3D joints, and corresponding plurality of prediction errors, wherein the plurality of final 3D joints is concatenated to generate the 3D pose.