3D Human Mesh Recovery With Cross-Attention for Multi-Person Scenes
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Solution Overview
Problem
Existing methods for whole-body mesh recovery from a single image are inefficient and sensitive to local minima, particularly in real-time applications, and struggle with capturing detailed poses of hands and faces, while multi-person settings face challenges with occlusions and truncation.
Innovation Solution
A method and system using a cross-attention module decoder to predict body model and depth parameters for multiple humans in a 3D scene from a single 2D image, incorporating a camera intrinsics encoder for improved accuracy and efficiency, enabling single-shot multi-person whole-body mesh recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optimization-based approaches like SMPLify-X are used for mesh recovery, then accuracy can be improved, but processing speed deteriorates and the method becomes sensitive to local minima
Solution Approach 1:
The patent replaces traditional optimization-based mechanical iterative processes with a deep learning-based end-to-end regression system. The network directly predicts 3D mesh parameters from 2D images without iterative optimization, eliminating local minima issues and achieving real-time processing speed while maintaining accuracy through learned feature representations and cross-attention mechanisms.
Solution Approach 2:
The patent changes the fundamental approach from iterative parameter optimization to direct parameter prediction through neural network regression. By transforming the problem into a direct mapping from image features to mesh parameters, the system achieves both speed and accuracy without the computational burden of optimization loops.
2Measurement precision
If multi-crop pipelines are used to capture hands and faces, then detail accuracy improves, but system complexity and processing time increase
Solution Approach 1:
The patent merges the processing of entire-body and detailed body parts (hands and faces) into a single unified neural network framework. The cross-attention module integrates features from different body regions simultaneously, eliminating the need for separate cropping, processing, and aggregation steps while maintaining high accuracy for detailed poses.
Solution Approach 2:
The patent creates a universal decoder that handles both entire-body and detailed body part predictions within a single model. The cross-attention mechanism enables the network to adaptively focus on relevant body regions (including hands and faces) without requiring separate specialized modules, reducing complexity while preserving detail accuracy.
3Productivity
If single-person learning-based methods are used, then processing efficiency improves, but applicability to multi-person scenes deteriorates
Solution Approach 1:
The patent segments the image into multiple regions corresponding to different persons and processes each region independently through the network. The detector identifies N persons in N respective regions, and the decoder processes features for each person separately, enabling efficient multi-person handling while maintaining single-person processing speed through parallel computation.
Solution Approach 2:
The patent extends single-person methods to multi-person scenes by adding the dimension of person count (N). The system processes N persons simultaneously by detecting N regions and generating N sets of mesh parameters through the same network architecture, achieving both efficiency and versatility through scalable parallel processing.
Data Source
AI summary
A method for recovering a 3D mesh of N humans in a 3D scene comprises: encoding a 2D image from an image capturing device to extract embedded features for each of a plurality of regions; detecting N humans in N respective regions among the plurality of regions; processing the embedded features in the N respective regions and the embedded features for each of the plurality of regions to predict body model and depth parameters using a decoder comprising a cross-attention module; providing the predicted body model parameters to a 3D parametric model for generating 3D meshes; and placing the generated 3D meshes at respective 3D spatial locations based on the predicted depth parameters.