betekintés - Computer Science - # 3D Human Mesh Reconstruction

W-HMR: Human Mesh Recovery in World Space with Weak-supervised Camera Calibration and Orientation Correction

Q: How does the weak-supervised approach impact the accuracy of focal length prediction?

The weak-supervised approach impacts the accuracy of focal length prediction by leveraging human body distortion information to predict a "reasonable" focal length for full-perspective projection. Instead of predicting the actual focal length, which is an ill-posed problem due to various influencing factors in 3D-to-2D projection, the model learns to predict a reasonable focal length based on body distortion. This method eliminates the need for rare and diverse focal length labels, making it more suitable for training models accurately. By focusing on aligning reconstructed 3D human bodies with original images through precise mesh-image alignment, the weak-supervised approach significantly improves recovery accuracy.

Q: What are the implications of decoupling global body recovery into different components?

Decoupling global body recovery into different components has several implications that enhance overall reconstruction results: Camera Calibration: By separating camera calibration from local body recovery and global orientation correction, errors in camera parameter estimation do not accumulate during model training and inference. This ensures finer mesh-image alignment and more accurate 2D supervision. Local Body Recovery: Focusing on local body recovery allows for optimal pose and shape accuracy in camera coordinate without being affected by incorrect camera parameters or distortions. Global Body Orientation Correction: The orientation correction module corrects predicted body orientations in world coordinates based on spatial features, global information, and initial predictions from previous stages. This ensures that reconstructed human poses remain normal and reasonable in world space. By decoupling these components, W-HMR can consider both accuracy in camera coordinate and reasonableness in world coordinate simultaneously, expanding its range of applications while achieving high-quality reconstruction results.

Q: How does the proposed orientation correction module enhance reconstruction results?

The proposed orientation correction module enhances reconstruction results by correcting predicted body orientations in world coordinates after initial predictions have been made using spatial features from previous stages such as camera rotation matrices (Rc), global information (glob_info), and predicted SMPL parameters (Θ). Here's how it enhances reconstruction results: Maintains Reasonable Poses: The OrientCorrect module ensures that reconstructed human poses remain normal and reasonable in world space even when there are discrepancies between camera coordinate predictions and real-world orientations. Effective Correction Mechanism: By utilizing FC layers to adjust rotations instead of direct multiplication like traditional methods do, OrientCorrect effectively corrects slight or significant deviations caused by inaccurate initial predictions or complex shooting conditions. Simplicity & Efficiency: The simplicity of this indirect approach makes it easy to integrate into existing models without compromising performance or structure integrity while improving overall reconstruction quality. Overall, the orientation correction module plays a crucial role in ensuring accurate reconstructions across dual coordinate systems by addressing issues related to erroneous pose estimations caused by incorrect camera parameters or distortions present during image capture scenarios like those encountered with SPEC-MTP dataset challenges mentioned earlier.

Alapfogalmak

Decoupling global body recovery into camera calibration, local body recovery, and global body orientation correction improves accuracy and reasonableness in world space.

Kivonat

The article introduces W-HMR, a method for 3D human mesh recovery that addresses challenges in reconstructing bodies from monocular images. It focuses on weak-supervised camera calibration and orientation correction to achieve high-quality reconstruction in both camera and world coordinates. The method decouples the process into three parts: camera calibration, local body recovery, and global body orientation correction. By leveraging body distortion information for focal length prediction and introducing an orientation correction module, W-HMR expands the range of applications for accurate reconstruction.

Index:

Introduction to 3D human mesh recovery.
Challenges in traditional methods based on camera coordinate.
Proposal of W-HMR method focusing on weak-supervised camera calibration.
Description of the orientation correction module for reasonable poses in world space.
Training paradigm with three stages: regressors training, FULL2 2D supervision, and OrientCorrect training.
Implementation details including feature extraction and regression modules.
Evaluation results on distorted datasets (AGORA, HuMMan, SPEC-MTP) in both camera and world coordinates.
Comparison with SOTA methods on traditional benchmarks (3DPW, H36M, MPII-INF-3D).
Metrics used for evaluation: MPJPE, MVE, NMJE, NMVE in camera coordinate; W-MPJPE, PA-MPJPE, W-PVE in world coordinate.

Összefoglaló testreszabása

Átírás mesterséges intelligenciával

Hivatkozások generálása

Forrás fordítása

Egy másik nyelvre

Gondolattérkép létrehozása

a forrásanyagból

Forrás megtekintése

arxiv.org

Statisztikák

Compared to SPEC [19], our model achieves a W-MPJPE of 118.7 on SPEC-MTP dataset.
The proposed method outperforms existing models with a PA-MPJPE of 66.6 on SPEC-MTP dataset.

Idézetek

"Our contribution can be grouped into the following three points..."
"We propose a weak-supervised camera calibration method..."

Főbb Kivonatok

W-HMR

by Wei Yao,Hong... : arxiv.org 03-26-2024

https://arxiv.org/pdf/2311.17460.pdf

Mélyebb kérdések

How does the weak-supervised approach impact the accuracy of focal length prediction?

The weak-supervised approach impacts the accuracy of focal length prediction by leveraging human body distortion information to predict a "reasonable" focal length for full-perspective projection. Instead of predicting the actual focal length, which is an ill-posed problem due to various influencing factors in 3D-to-2D projection, the model learns to predict a reasonable focal length based on body distortion. This method eliminates the need for rare and diverse focal length labels, making it more suitable for training models accurately. By focusing on aligning reconstructed 3D human bodies with original images through precise mesh-image alignment, the weak-supervised approach significantly improves recovery accuracy.

What are the implications of decoupling global body recovery into different components?

Decoupling global body recovery into different components has several implications that enhance overall reconstruction results:

Camera Calibration: By separating camera calibration from local body recovery and global orientation correction, errors in camera parameter estimation do not accumulate during model training and inference. This ensures finer mesh-image alignment and more accurate 2D supervision.

Local Body Recovery: Focusing on local body recovery allows for optimal pose and shape accuracy in camera coordinate without being affected by incorrect camera parameters or distortions.

Global Body Orientation Correction: The orientation correction module corrects predicted body orientations in world coordinates based on spatial features, global information, and initial predictions from previous stages. This ensures that reconstructed human poses remain normal and reasonable in world space.

By decoupling these components, W-HMR can consider both accuracy in camera coordinate and reasonableness in world coordinate simultaneously, expanding its range of applications while achieving high-quality reconstruction results.

How does the proposed orientation correction module enhance reconstruction results?

The proposed orientation correction module enhances reconstruction results by correcting predicted body orientations in world coordinates after initial predictions have been made using spatial features from previous stages such as camera rotation matrices (Rc), global information (glob_info), and predicted SMPL parameters (Θ).
Here's how it enhances reconstruction results:

Maintains Reasonable Poses: The OrientCorrect module ensures that reconstructed human poses remain normal and reasonable in world space even when there are discrepancies between camera coordinate predictions and real-world orientations.

Effective Correction Mechanism: By utilizing FC layers to adjust rotations instead of direct multiplication like traditional methods do, OrientCorrect effectively corrects slight or significant deviations caused by inaccurate initial predictions or complex shooting conditions.

Simplicity & Efficiency: The simplicity of this indirect approach makes it easy to integrate into existing models without compromising performance or structure integrity while improving overall reconstruction quality.

Overall, the orientation correction module plays a crucial role in ensuring accurate reconstructions across dual coordinate systems by addressing issues related to erroneous pose estimations caused by incorrect camera parameters or distortions present during image capture scenarios like those encountered with SPEC-MTP dataset challenges mentioned earlier.