3D Pose Estimation With Projective Attention for Crowded Scenes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current 3D pose estimation techniques struggle with scalability and accuracy when predicting the poses of multiple people in a scene from multi-view images, as they are computationally expensive and rely heavily on 2D predictions, often omitting vital appearance information and are not efficient for crowded scenes.
Innovation Solution
A neural network system comprising an encoder and decoder neural network with a projective-attention mechanism that extracts multi-view image features, regressesively refines 3D joint locations, and uses adaptive deformable sampling to gather localized context information, incorporating hierarchical joint query embeddings and positional encoding to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cross-view matching is used to aggregate 2D poses for 3D reconstruction, then 3D pose estimation can be achieved, but computational cost grows linearly with the number of people making it non-scalable
Solution Approach 1:
The system segments the 3D pose estimation problem by directly predicting 3D joint locations for each person independently using a two-stage detection framework, avoiding the need for cross-view matching between all pairs of people. This segmentation approach breaks the linear computational complexity by eliminating the combinatorial matching process.
Solution Approach 2:
The system introduces a unified 3D joint confidence volume as an intermediary representation that aggregates information from multiple camera views. This volume serves as a mediator that combines multi-view data before final 3D pose prediction, replacing the direct cross-view matching approach with a more efficient volumetric aggregation mechanism.
2Reliability
If volumetric representation is used to estimate 3D human proposals, then cross-view matching issues are alleviated, but computational cost increases especially in large and sparse scenes
Solution Approach 1:
The system uses a two-stage detection framework where the first stage generates candidate 3D human proposals with a unified confidence volume, and the second stage refines only the relevant candidates. This partial action approach processes only necessary portions of the data at each stage, reducing overall computational complexity compared to processing all possible combinations.
Solution Approach 2:
The system performs preliminary 3D human proposal generation and filtering in the first stage before conducting detailed per-person joint estimation. By pre-processing and filtering candidates beforehand, the system reduces the computational burden of the second stage, especially in large and sparse scenes.
3Measurement precision
If two-stage detection framework is used with per-person joint estimation, then 3D pose accuracy can be improved, but processing time scales with the number of people in the scene
Solution Approach 1:
The system merges the 3D joint estimation process across all people by using a unified confidence volume that aggregates information from all camera views simultaneously. This combining approach allows parallel processing of multiple people's poses, reducing the time penalty that would otherwise scale linearly with the number of people.
Solution Approach 2:
The system maintains continuous refinement of 3D joint positions through the two-stage framework, where the first stage produces initial proposals and the second stage continuously refines them. This continuous action ensures high accuracy while optimizing the time spent at each refinement level.
Data Source
AI summary
A system for 3D pose estimation of people is described, comprising an encoder neural network configured to extract multi-view image features from 2D input images obtained from different camera views, and a decoder neural network configured to receive the multi-view image features and predict 3D joint locations of the people in the 2D input images. The decoder neural network comprising a projective-attention mechanism configured to: determine a 2D projection of a predicted 3D joint location for the camera views and assign them as anchor points; apply an adaptive deformable sampling strategy to gather localized context information of the camera views and to learn deformable offsets, and based on the deformable offsets, determine the deformable points for the anchor points; generate attention weights based on the multi-view image features at the anchor points; and apply the attention weights to aggregate the multi-view image features at the deformable points.