Spatio-Temporal Pose Transformer for Blur and Occlusion Robustness
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Solution Overview
Problem
Existing 3D human pose and shape estimation methods face challenges in handling motion blur and occlusion, leading to unstable performance due to excessive computational complexity and spatial information compression, particularly in image sequences.
Innovation Solution
A spatio-temporal preservation transformer that aligns features spatially and models spatio-temporal relationships using a transformer architecture, incorporating a spatial alignment module, space2batch module, and uncertainty-guided attention re-weighting to minimize computational complexity and enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If global average pooling is applied to compress spatial information, then computational complexity is reduced, but spatial information is lost and reconstruction errors increase
Solution Approach 1:
The patent segments the feature processing into two distinct stages: spatial alignment (warping features to a common coordinate system) and temporal relationship modeling. This segmentation allows spatial information to be preserved through alignment operations rather than compressed through pooling, while still enabling efficient temporal processing in the second stage.
Solution Approach 2:
The patent transforms the problem from directly modeling spatio-temporal relationships in a high-dimensional space to first aligning spatial dimensions across frames, then modeling temporal relationships in a normalized temporal dimension. This dimensional transformation reduces complexity without sacrificing spatial fidelity.
2Measurement precision
If spatio-temporal attention is applied to model temporal relationships, then temporal accuracy is improved, but computational complexity increases excessively
Solution Approach 1:
The patent performs spatial alignment as a preliminary action before applying temporal attention mechanisms. By pre-aligning the spatial coordinates of features across different frames through warping operations, the system simplifies the subsequent temporal attention computation, as features from different frames now correspond to the same spatial locations, reducing the complexity of spatio-temporal attention.
3Device complexity
If single-frame-based estimation is used, then computational complexity is low, but performance becomes unstable due to motion blur and occlusion
Solution Approach 1:
The patent processes image sequences continuously by modeling temporal relationships across multiple frames using attention mechanisms. This continuous processing allows the system to leverage information from multiple time steps, maintaining stable predictions even when individual frames suffer from motion blur or occlusion, as the temporal context provides compensatory information.
Data Source
AI summary
A method and system for providing a spatio-temporal preservation transformer for 3D human pose and shape estimation may provide a transformer that considers both spatial and temporal dimensions and minimizes computational complexity when estimating a 3D human pose and shape based on an image sequence such as a video, thereby improving the data processing efficiency and performance required for the 3D human pose and shape estimation based on the image sequence, enhancing the quality of the resulting data and improving various application services and related industrial environments.