Neural Network Human Mesh Estimation Using Dense Depth and UV Annotations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for training neural networks to predict human mesh model parameters in monocular settings lack effective use of dense depth information, which is crucial for accurate 3D human pose and shape estimation.
Innovation Solution
A system that utilizes dense depth and part-based UV map annotations to generate an effective training signal for neural networks, incorporating a depth loss module and a rigid transform loss module to align human mesh models with real-world images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense depth information is introduced during training, then estimation accuracy is improved, but data collection complexity increases
Solution Approach 1:
The patent introduces a depth estimation module as an intermediary component that generates pseudo-depth labels from 2D images. This mediator enables the system to obtain dense depth information without requiring expensive RGB-D cameras or motion capture systems, thus resolving the contradiction between improving estimation accuracy and reducing data collection complexity
Solution Approach 2:
The patent creates synthetic depth data by projecting 3D mesh models onto 2D images and generating corresponding depth maps. This copying approach allows the system to generate training data with accurate depth information from standard 2D image datasets, eliminating the need for complex depth sensing hardware while maintaining high estimation accuracy
2Manufacturing precision
If part-based UV map annotations are used, then alignment precision is improved, but annotation difficulty increases
Solution Approach 1:
The patent implements a self-service annotation mechanism where the system automatically generates part-based UV map annotations by projecting mesh vertex coordinates onto the image plane. This eliminates the need for manual annotation of correspondences between 3D mesh parts and 2D image regions, significantly reducing annotation difficulty while maintaining high alignment precision through automated computation
Solution Approach 2:
The patent replaces manual annotation processes with automated computational geometry operations. Instead of requiring annotators to manually identify and mark corresponding points between 3D mesh and 2D images, the system automatically computes UV coordinates and generates annotations through mathematical projection and transformation operations
3Measurement precision
If depth loss module is applied, then pose estimation accuracy is improved, but training complexity increases
Solution Approach 1:
The patent segments the training objective into multiple independent loss components: depth loss for 3D position accuracy, UV loss for texture alignment, and pose loss for joint location accuracy. This segmentation allows each loss function to focus on specific aspects of the estimation problem, making the training process more manageable and less complex while improving overall pose estimation accuracy through combined optimization
Data Source
AI summary
A system for training neural networks that predict the parameters of a human mesh model is disclosed herein. The system includes at least one camera and a data processor configured to execute computer executable instructions for: receiving a first frame and a second frame of a video from the at least one camera; extracting first and second image data from the first and second frames of the video; inputting the sequence of frames of the video into a human mesh estimator module, the human mesh estimator module estimating mesh parameters from the sequence of frames of the video so as to determine a predicted mesh; and generating a training signal for input into the human mesh estimator module by using a two-dimensional keypoint loss module that compares a first set of two-dimensional image-based keypoints to a second set of two-dimensional model-based keypoints.