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PaMIR: Parametric Model-Conditioned Implicit Representation for Image-Based Human Reconstruction.

Zerong Zheng, Tao Yu, Yebin Liu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|January 12, 2021

Summary

This summary is machine-generated.

This study introduces Parametric Model-Conditioned Implicit Representation (PaMIR) for accurate 3D human reconstruction from single images. PaMIR enhances generalization for challenging poses and clothing, achieving state-of-the-art results.

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Area of Science:

Computer Vision
3D Reconstruction
Machine Learning

Background:

Accurate 3D human modeling from single images is a complex, ill-posed problem.
Existing 3D representations have limitations in handling diverse poses and clothing.

Purpose of the Study:

To develop a novel 3D human representation and reconstruction framework.
To improve the robustness and accuracy of 3D human modeling from single images.

Main Methods:

Proposed Parametric Model-Conditioned Implicit Representation (PaMIR), combining parametric body models with deep implicit functions.
Developed a novel deep neural network to regularize implicit functions using parametric model semantics.
Integrated a depth-ambiguity-aware training loss and a body reference optimization method.

Main Results:

Achieved improved generalization for challenging poses and clothing topologies.
Enabled successful surface detail reconstruction by resolving depth ambiguities.
Demonstrated state-of-the-art performance in image-based 3D human reconstruction.

Conclusions:

PaMIR offers a robust and generalizable approach for 3D human reconstruction.
The framework can be extended to multi-image scenarios without complex calibration.
The method significantly advances single-image 3D human modeling capabilities.